The user wants a full UCI chess engine in Python. Let me think about this carefully — this is a big task. I need to write: 1. A board representation with full chess rules: - Move generation (pseudo-legal + legality check) - Castling, en passant, promotion - Check detection, checkmate, stalemate - Fifty-move rule, threefold repetition (for UCI "go" handling at least) - FEN parsing and output (needed for `position` and `d` commands) 2. Search: - Minimax with alpha-beta pruning - Iterative deepening - Quiescence search - Move ordering (MVV-LVA, killer moves, history heuristic, hash moves from TT) - Transposition table with Zobrist hashing - Null-move pruning (optional but good) - Check extensions - Late move reductions (LMR) — "much fine-tuned search policy" - Aspiration windows in iterative deepening - Mate distance pruning 3. Evaluation: - Material - Piece-square tables (tapered eval, middlegame/endgame) - Pawn structure (doubled, isolated, passed pawns) - Mobility (light) - King safety (simple: pawn shield, castled bonus) - Rook on open/semi-open files - Bishop pair 4. UCI protocol: - `uci` -> `id`, `uciok` - `isready` -> `readyok` - `ucinewgame` - `position startpos|fen ... moves ...` - `go` with parameters: depth, movetime, wtime, btime, winc, binc, infinite, nodes, mate (basic handling) - `stop` — need to respect stop during search (search in chunks or check time/nodes periodically) - `ponderhit` (can just ignore or handle) - `quit` - `go infinite` + `stop` should work - Output: `info depth ... score cp ... nodes ... nps ... time ... pv ...` and `bestmove` Requirements: - "No error in runtime" — robust: handle exceptions gracefully, validate FEN, handle malformed input, never crash. Wrap the UCI loop in try/except to be safe. - Single file, pure Python (no external deps like python-chess — user likely wants self-contained). Pure Python speed is a concern but let's make it as efficient as reasonably possible: 0x88 board or 10x12 mailbox, incremental evaluation maybe too complex — I'll keep it simpler but efficient enough. Use arrays, precompute move offsets, use integers for pieces. Design decisions: Board representation: I'll use a 120-square mailbox (10x12) for easy off-board detection. Pieces: use integers. Empty = 0, wP=1, wN=2, wB=3, wR=4, wQ=5, wK=6, black = negative (bP=-1 etc.). This makes color checks trivial (sign). Actually, let me think about speed. Pure Python engines get maybe 10k-50k nps in the search. That's fine for a demo engine. Use lists, avoid classes for hot paths (or use plain attributes). Use local variable caching in hot loops. Move generation: generate pseudo-legal moves, then make/unmake and check king capture legality (i.e., after making a move, check if own king is attacked). For legality: standard approach — make move, check if the mover's king is in check, if so, illegal. Attack detection: `is_square_attacked(sq, by_side)` — check knight jumps, king, pawn attacks, sliding pieces along rays. Make/unmake: keep a stack of undo info (captured piece, castling rights, ep square, halfmove clock, hash key). Zobrist hashing: random 64-bit numbers for piece-square, side to move, castling rights (4 bits or 16 combos), en passant file. Incrementally update in make/unmake. Search: - Iterative deepening loop from depth 1. - Alpha-beta negamax (negamax formulation of minimax — the user asked for minimax; negamax is a variant of minimax; I can mention that. Maybe I should literally do minimax with alpha-beta... negamax is cleaner and equivalent. I'll implement negamax and note it's the standard efficient form of minimax with alpha-beta. Or to satisfy "minimax" literally, I could write it in minimax form. I think negamax with a comment explaining is fine — most engines call it alpha-beta negamax which is minimax. I'll note in comments.) - Transposition table: dict keyed by hash -> (depth, flag, score, best_move). Use depth-preferred replacement. - Move ordering: TT move first, then captures sorted by MVV-LVA (and maybe SEE but skip SEE for simplicity — or implement simple SEE? MVV-LVA is fine), promotions, killers, history. - Quiescence search: captures and queen promotions, with stand-pat and delta pruning. - Check extension: extend when in check. - Null move pruning: skip if in check or few pieces; R = 2 + depth/6 or so. - LMR: for moves beyond first few, at depth >= 3, reduce by one (or two at higher depths), re-search if score > alpha. - Futility pruning: at shallow depths, prune quiet moves when eval + margin <= alpha. Need to be careful with legality — since we test legality after make, pruning before legality check could cause issues only if we return a wrong score; actually futility pruning returns alpha without making the move — that's fine, standard. But must not prune when in check. - Aspiration windows around previous score; widen on fail. - Time management: check time every N nodes; also allocate time based on wtime/btime + inc, capped. Soft limit vs hard limit: stop search when soft limit exceeded (finish current depth only if hard not exceeded... simpler: check hard time limit in search; for depth completion, use soft limit between iterations). - Mate scores: adjust to print "mate N" in UCI. Score encoding: use large values (e.g., 100000 + ply for mate scores so it's distance from root). TT score adjustments for mate scores relative to node (store mate scores adjusted by ply) — need to handle the ply-relative mate score: store score adjusted to node-relative; I'll handle standard adjustment. Also need repetition detection in search: keep a history of hashes; if current hash appeared before in the game/search path, score as draw (0) — important to avoid blundering into repetition and to detect draws. Keep game history hashes from `position` moves plus search path. Threefold: within search, treat first repetition as draw (common practice). Fifty-move rule: if halfmove clock >= 100, score draw. (Actually careful — checkmate on the 100th halfmove counts; but keep simple: >= 100 draw unless the side is in checkmate? For simplicity, treat >=100 as draw when not checkmate. Fine for demo.) Evaluation (tapered): - Material: P=100, N=320, B=330, R=500, Q=900. - PSTs for each piece, mg and eg, interpolate by phase. - Bishop pair bonus. - Doubled pawns penalty, isolated pawns penalty, passed pawns bonus (scaled by rank, bigger in endgame). - Rooks on open files bonus, semi-open small. - King safety: pawn shield in middlegame; king PST covers centralization in endgame. - Mobility: quick knight/bishop mobility small bonus? Maybe skip mobility for speed, or add cheap mobility for bishops/rooks. I'll add simple mobility for knights and bishops (count moves) — could be slow; PSTs partially cover it. I'll include light mobility for bishops and rooks via slider scan — actually it's already needed for rook open files (need to scan pawns). Let me just compute pawn counts per file cheaply, and skip mobility to keep speed. Hmm, "much fine-tuned" mainly refers to search policy. I'll include a decent eval with pawn structure, king safety, bishop pair, rook files, tempo. Actually let me add a modest mobility term using attack offsets — for knights: count empty/enemy squares reachable (8 offsets check). That's 8 lookups per knight; bishops: sliding until blocked, count. Could add ~cost. I'll include knight mobility cheap. Maybe skip to keep code tighter. I'll include it — it's fine. Insufficient material draw: K vs K, K+B vs K, K+N vs K, K+B vs K+B same color... I'll do simple: if no pawns and material very low, draw. Simple check: both sides have no pawns and no queens/rooks and minors <= 1 each -> draw 0. Keep simple. Move encoding: tuple (from, to, promo, flags)? Tuples are convenient but allocation-heavy. Use ints: from | to<<7 | promo<<14 | flags<<18? For speed, encode move as integer: frm (0-119) needs 7 bits, to 7 bits, promo 3 bits, flag bits. I'll write encode/decode helpers inline. Int encoding is faster for TT storage too and for killer comparison. Let's do: move = frm | (to << 7) | (promo << 14) | (flags << 17) flags: 0 = normal, 1 = en passant, 2 = double pawn push, 3 = castle. Promo: 0 none, else piece code (N=2,B=3,R=4,Q=5). Make move needs: captured piece (from board), handle ep capture (remove pawn at ep target-10 direction), castling rook move, promotion. Update castling rights (mask table per square), ep square (on double push), halfmove clock, hash. Castling rights update via table: rights &= castle_mask[from] & castle_mask[to]. En passant hash: only include ep file if there's an enemy pawn able to capture? Simpler: always hash ep file when set. Minor inconsistency risk for repetition detection — acceptable, but can cause missed repetition detection; fine. Legality: after make, check `is_square_attacked(king_sq[side_moved], side_to_move_now)`. Track king squares incrementally in make/unmake. Pseudo-legal generation: - Pawn: single push, double push (from rank 2/7 in mailbox indices), captures (two diagonals), promotions (push/capture to last rank), en passant (if ep square set and pawn attacks it). - Knight/King: offsets, target not own piece and on-board. - Sliders: loop offsets until off-board or piece; capture if enemy. - Castling: rights present, squares between empty, king not in check, squares king passes through not attacked (e1,f1,g1 for O-O — e1 and f1 checked; g1 is destination checked by legality after make... but standard: check e1, f1, g1 not attacked; we can check e1 not attacked (not in check), f1 not attacked, g1 not attacked before generating). Also squares empty: f1,g1 for O-O; d1,c1,b1 for O-O-O (b1 also must be empty; king passes c1,d1 — check c1,d1 not attacked). Mailbox indices: Let me set 10x12 board: 120 squares, rank/file mapping: sq = 21 + file + 10*rank? Let me define: A1 = 21, H1 = 28, A2 = 31 ... A8 = 91, H8 = 98. Off-board squares contain a sentinel (e.g., 7 or None). I'll use value 99 (OFFBOARD) as a distinct value; board is a list of 120 ints. Direction offsets: N=+10 (from white's perspective, +10 = up one rank), S=-10, E=+1, W=-1, NE=+11, NW=+9, SE=-9, SW=-11. Knight: ±8, ±12, ±19, ±21. King/queen: all 8. Pieces: EMPTY=0, WP=1, WN=2, WB=3, WR=4, WQ=5, WK=6, BP=-1, etc. OFFBOARD sentinel = 7 (a value that's neither own piece (positive small) nor enemy). Check "is own piece": piece > 0 and < 7 for white... hmm OFFBOARD=7 conflicts. Let me use OFFBOARD = 100. Then for white, "target == 0 or target < 0" = can move; "target < 0" = capture. For black, "target == 0 or (0 < target < 7)". Since OFFBOARD=100 > 6, sign checks: white piece p: 1..6; black: -1..-6; empty 0; offboard 100. For white moving: if target == 100 skip; if target <= 0 ok (empty or enemy); if target >= 1 and <= 6 own. Works with `if piece == OFF: break/continue`. FEN parsing: standard; handle position command with "startpos" or "fen <...> moves ...". Also implement `d` command? UCI `d` is optional ("d" is in the UCI spec? Actually `d` is part of the UCI spec as a debug command — display current position. GUIs rarely use it. I'll implement it: prints board + FEN.) Also `eval` debug command optional. I'll add `d`. `setoption` — ignore gracefully (log "info string" or just accept). `register` -> respond nothing needed. `debug` — ignore. Ponder: support `ponder` in go? I can accept the token and ignore (search anyway). `ponderhit` — ignore. To be safe, handle: if ponder was on, ponderhit makes it switch to normal time; simplest: treat ponder search as normal search but don't print bestmove until stop or time? That complicates. Many simple engines just ignore ponder. I'll accept `go ponder` and search normally, and on `ponderhit` do nothing special (the engine would have already output bestmove — technically wrong but harmless for most GUIs). Hmm — printing bestmove during ponder is a protocol violation that can confuse GUIs. Safer: if `go ponder`, run the search but don't emit bestmove; wait for `ponderhit` (then emit bestmove immediately with the finished result) or `stop` (emit bestmove). Implementation: run search with a flag; after search, if pondering, hold the bestmove until ponderhit/stop. Need search to still be interruptible... Since our search runs synchronously in the same thread, holding bestmove blocks reading input. I'd need a thread reading input. To keep things robust and simple: run search in the main loop synchronously, but check input? Can't easily poll stdin cross-platform without threads. Option: run the search in a separate thread, main thread reads stdin and handles stop/quit/ponderhit via a flag. That's the cleanest for correct `stop` handling too. Threading in Python with a simple flag (search_stop_event) is fine. Let me do: - Main thread: reads stdin lines, dispatches. - Search thread: launched on `go`; sets globals for best move when done; prints info/bestmove. - `stop` sets stop_flag (and event), search checks periodically and exits, printing bestmove from last completed iteration (or best so far). - `quit` sets stop flag and exits. - `isready` during search: must reply readyok — with threads, easy. (Without threads, isready during search would be delayed — protocol says GUI won't send isready during search usually, but threads make us robust.) - `go infinite`: search runs until `stop`. - `ponderhit`: if pondering, switch to normal time management (set time limit), else ignore. Threading concerns: GIL means the search thread runs; main thread blocks on input() reading — fine. Shared state: use a lock or just simple flags (Python assignment is atomic enough). Set `engine.searching = True`, etc. On `position` or `ucinewgame` during search: UCI says GUI should not, but to be robust, stop search first, ignore result. I'll keep ponder simple: support `go ponder` by searching without printing bestmove at the end; wait. If `ponderhit` arrives: apply normal time limits (set stop time), let search continue until time/stop. If `stop` arrives: stop, print bestmove. If any other command (position/quit): stop, don't print bestmove (quit) — per UCI, when a `position`/`ucinewgame` comes while pondering, engine should stop searching without printing bestmove. This is getting complex but it's the "all UCI required standard" ask. Let me implement a reasonably faithful subset: Commands: uci, isready, ucinewgame, position, go (with depth, movetime, wtime, btime, winc, binc, infinite, nodes, ponder, mate), stop, ponderhit, quit, d (debug), register/setoption/debug ignored gracefully. Time management: - If movetime: search exactly that long (soft limit; hard limit slightly above). - If wtime/btime: allocate = time/30 + inc*3/4? Common: allocated = time/25 + inc/2, capped at time/4 or so. Hard limit = min(time/3, ...). Ensure at least ~50ms if time is low. Let's do: base = time/30, + inc*0.8; cap to max(50, time/8?) Hmm. Simple robust: - soft = max(20, time // 30 + inc // 2) - hard = max(soft + 20, min(time // 5, soft * 5)) and ensure hard < time - 50 buffer if possible. - If infinite/depth/nodes given: no time limits (except stop). - Node limit: check node count. - Depth limit: from `go depth N` or `go mate N` (mate N: search until mate found — can treat as depth search with target; simplest: search up to some max depth and stop early when a mate score with distance <= N found. I'll implement basic mate handling: iterate; if score indicates mate in <= N for us, stop.) Check time every 1024 nodes or so; when exceeded, raise an exception or set flag returning; I'll use a flag and check in the search loop — long qsearch could delay; also check in qsearch every so often. Use an exception? Exceptions in Python are actually a decent mechanism to unwind quickly: define SearchTimeout exception. Raise from the node check. But raising through many recursion levels is fine with try in the ID loop. However, exception overhead at raise-time only — fine. But careful: state must be restored via finally? make/unmake pattern: if exception raised mid-search, the board state (move stack) is corrupted for the copy we search on. Solution: search on a copy of the position. Copy the board list (list.copy()) and state. That's clean: `search_pos = pos.copy()` (need a copy method). Then even if we abandon mid-search, original position intact. TT and history unaffected. So design: class Position with fields: board (list 120), side (1 white, -1 black), castling (int bits), ep (square or 0/None), halfmove, fullmove, king_sq dict or two vars wk_sq, bk_sq, hash, history (list of hashes for repetition), plus move stack for unmake. Provide copy() that copies board list and scalar fields (history list can be shared? For repetition we need game history — copy reference of game history plus per-search path... Let me store `self.hist` as list of hashes of positions after each make; game history loaded from position command; search adds its path. On copy, share the same hist? If search thread mutates hist while... it's on a copy; copy can take reference and we push/pop symmetrically; if timeout leaves pushes unpopped, hist is corrupted for the next search! Because it's the shared game history. So copy must duplicate hist list too (hist.copy()). It's a small list — fine. Actually simpler: separate game history (immutable per search, from Position setup) and search path (local list). Repetition check: count occurrences of current hash in game_history + current path. I'll keep one combined list in Position: `self.rep_hist` (hashes). On copy: `rep_hist = list(self.rep_hist)`. On make: append new hash; on unmake: pop. That works. Make/unmake stack: store tuples (frm, to, piece, captured, promo, flag, castling, ep, halfmove, hash, ...). Or an undo list of small tuples; fine. Unmake needs: previous castling rights, ep, halfmove, hash, captured piece and its square (for ep, captured pawn is on different square), rook move for castling. I'll store full undo record: (frm, to, moved_piece_after? , captured, cap_sq, promo, flag, castling, ep, half, hash). And king squares: track before/after? Just recompute in unmake: if moved piece was king, set back. If captured... king can't be captured (we never allow capturing king because legality ensures? Pseudo-legal move gen could generate king captures if previous move left king in check — but we only make legal moves in search; in perft of pseudo-legal we filter. However in qsearch / MVV we only play legal moves too. Since we always test legality after make before recursing... but the capture of the king could occur if we make a pseudo-legal move from a position where opponent king is capturable — that can't happen because previous ply made a legal move. Root position from FEN could be illegal; validate: if side to move's king missing or both kings missing — guard. Also handle "position" with moves that leave king in check — accept but search might be weird; add a guard: if no king for either side, return no moves / eval 0. Fine.) Actually simpler legality approach: generate pseudo-legal; in search, make the move; if own king attacked → unmake and skip. That's the standard. The "king capture" never happens since we skip illegal before recursing. Perft for testing (optional debug command `perft N`)? Could include as bonus — nice for verifying correctness. I'll include `perft` debug command — helps user validate. Keep it. Now search specifics ("much fine-tuned search policy"): Negamax with alpha-beta: ``` def search(pos, depth, alpha, beta, ply, can_null): check time/nodes if pos.halfmove >= 100 or repetition(pos) or insufficient material: return 0 (draw, but checkmate detection first? If in check and no moves it's mate — handled by move loop; draw checks before searching fine) if ply > 0 and (rep or fifty): return 0 mate distance pruning: alpha = max(alpha, -MATE + ply), beta = min(beta, MATE - ply - 1); if alpha >= beta return alpha TT probe: if entry.depth >= depth and ply>0 and not PV-ish? Standard: use TT cutoff if flag conditions and (not a PV node or be careful). I'll use TT cutoff for all nodes when depth sufficient, but avoid in root. Careful with TT + aspiration. It's standard; use it. if depth <= 0: return qsearch in_check = is_square_attacked(king, opp) if in_check: depth += 1 (check extension) static eval for pruning (only when not in check) null move: if not in_check and depth >= 3 and has non-pawn material and last move wasn't null: make null (flip side, clear ep, hash update), score = -search(depth-1-R, -beta, -beta+1, ply+1, can_null=False), unmake; if score >= beta: return beta (or score, careful with mate scores — return beta if score >= MATE_BOUNDS? Standard: if score >= beta: if score >= IS_MATE: return beta; else return score) Razoring? Optional — skip; futility in move loop instead. Generate moves, order (TT move first, MVV-LVA captures, promotions, killers, history). Move loop with: legal check after make futility pruning: if not in_check and not PV? and depth <= 2/3 and move is quiet and static_eval + margin(depth) <= alpha: skip quiet moves (after legality). Careful only when not in check and move not check... keep simple margin: 120*depth? Fine-tune: depth 1: 100, 2: 250, 3: 400? I'll use margins = {1:120, 2:280, 3:450} approx. Also skip if any move gave check? We don't know cheaply; can check after make: if opponent in check after move, don't prune. That costs an attack query per move — acceptable? It adds cost. Alternative: only prune quiets when depth <= 2 and eval + margin <= alpha and not in check and move doesn't give check (check via is_square_attacked(opp king, us) — one call). Acceptable. LMR: if depth >= 3 and move_index >= 4 and quiet and not in_check and not gives check: reduce depth-1-1 or -2 at deeper. Then if reduced score > alpha, re-search full depth. Implementation: reduction = 1 + (move_index > 12 and depth >= 5 ? 1 : 0) maybe. Re-search at full depth if score > alpha. Also for captures late: reduce slightly? Keep LMR for quiets and non-major captures. Standard approach fine. PVS: first move full window; subsequent: null window search; if alpha < score < beta: full re-search. If no legal moves: if in_check: return -MATE + ply else 0 (stalemate) TT store with flag (EXACT/LOWER/UPPER) and best move. Update killers/history on beta cutoff for quiets. ``` Quiescence: ``` def qsearch(alpha, beta, ply): check stop periodically in_check? If in check, must consider evasions — some engines do full evasions in qsearch. Simple version: if in check, search all moves (like a mini depth-0 full search with legal moves only, no stand pat). I'll implement: if in check: generate all moves; if none legal: return -MATE+ply; loop with alpha-beta, no stand-pat. Else: stand pat = eval; if stand_pat >= beta return; alpha = max; generate captures (+queen promos); MVV-LVA order; delta pruning: if stand_pat + value(captured) + 200 <= alpha: skip; make, legality check, recurse. Also TT for qsearch? Skip for simplicity/speed... could add; skip. ``` Eval details — PSTs. I need tables for 64 squares in 8x8, then map to 120-board. I'll define PSTs in 64-index form (rank-major, a1..h8 or a8..h1) and build lookup arrays of 120 entries at startup for white; black mirrors. Standard PST values (from CPW "Simplified Evaluation Function" by Tomasz Michniewski) — I'll use those as a base and add extras. Values: pawn, knight, bishop, rook, queen, king mg, king eg. I'll write them carefully. Let me define square mapping: For a 64 index i with file = i % 8, rank = i // 8 (rank 0 = 1st rank = white's back rank). Board 120 index: sq120 = 21 + file + 10 * rank. For black piece at 64-index i, mirrored rank: rank7 = 7 - rank → 64 index (7 - i//8)*8 + i%8. I'll precompute `PST_W[piece][sq120]` and `PST_B[piece][sq120]` or one array with sign... Let me build: for each piece code 1..6, table_pst_w[piece][sq120] (int), and black uses mirrored with negation in eval. Precompute both as flat lists of 120 lists: PST[piece] for white-view, and mirror inside. In eval: score from white's perspective: for each piece, add material + PST. Tapered eval: phase = sum of piece phase values (N=1, B=1, R=2, Q=4, total 24). eg weight = phase/24. score = (mg*phase + eg*(24-phase)) / 24. Doing full-board eval each qsearch node is slow but simple. Incremental material+PST updates in make/unmake would speed things a lot. Let me do incremental updates for material and PST (mg and eg scores) in make/unmake: track mg_score, eg_score (from white perspective) incrementally; pawns structure etc. computed in eval on the fly (needs pawn file scans — do it via file arrays? Track pawn counts per file per color incrementally too: wp_file[8], bp_file[8]; and passed-pawn detection needs rank info per file — track min/max? Complex. I'll compute pawn terms by scanning the 64 squares of the board each eval — the board scan is 64 lookups... plus piece scan. Alternative: maintain piece lists! Keep a list of piece locations per side? Maintenance complexity vs speed. Given "no error in runtime" is a priority and clarity matters, I'll do a hybrid: incremental material + PST (mg, eg), and eval computes structural terms by scanning pawn files using precomputed file lists: for each file, iterate squares 21+file + 10*k for k in 0..7 — 8 lookups per file * 8 files = 64 lookups, plus checking each. That's fine. Actually, let me simplify eval to keep it robust: - Incremental: mat_mg, mat_eg (material + PST, white perspective), also piece counts and phase; pawn counts per file (wp_count[file], bp_count[file]) maintained incrementally — then doubled/isolated/open files computed from these 16 ints cheaply. Passed pawns need frontmost pawn rank per file per side: maintain min rank of white pawns per file (most advanced) — incremental update is annoying when a pawn is removed from a file that has several (need rescan of that file). Rescan of one file = 8 lookups; that's fine: on pawn push/capture/promotion (which changes pawn structure), recompute that file's info for affected color by scanning 8 squares. Let me maintain: w pawn most-advanced rank per file? For passed pawn detection, standard is: pawn is passed if no enemy pawn on same or adjacent files at rank >= its rank (for white). With per-file enemy frontmost info (for black pawns, the minimal rank per file = most advanced black pawn; a white pawn at (f, r) is passed if for files f-1..f+1, black's min rank > r... black pawns move down; most advanced black pawn = smallest rank index. White pawn at rank r is passed if black has no pawn with rank <= r... wait black pawn at rank <= r? Black pawns at ranks less than or equal to r on files f-1, f, f+1 block the passer (they're in front of it from white's perspective if their rank > r? No: white moves up, ranks increase. A black pawn at rank > r is in front. Hmm: white pawn at rank r (0=rank1). Enemy black pawns that stop it are those with rank > r (in front) on files f-1, f, f+1. Black pawn on same file at rank > r blocks directly; adjacent files at rank > r also guard the path (can capture). So white pawn is passed if for files f-1..f+1: black's minimal rank... black's most advanced pawn on those files is minimal rank index? Black pawns start at rank 6 (index 6? rank index 1 = rank 2... let me use rank indices 0..7 = ranks 1..8). Black pawns at rank 6 initially and move down toward 0. "In front of white pawn at rank r" means black pawn rank > r. Black's most advanced pawn on a file = min rank index. So condition for passed: for each file ff in {f-1,f,f+1}: bp_min_rank[ff] is None or bp_min_rank[ff]... wait we need "no black pawn with rank > r", i.e., not exists. The most advanced black pawn is min rank. If min_rank <= r, there exist black pawns at ranks <= r — those are behind the white pawn (rank <= r means at or below it; a black pawn at same square impossible; at rank == r same file impossible since occupied; at adjacent file rank == r is beside, not blocking). Hmm, careful: a black pawn adjacent file at rank == r doesn't block (it can't capture backwards). Black pawns at rank < r are behind — don't block. Black pawns at rank > r on f±1 can capture the pawn as it advances, so they block. So condition: for ff in {f-1, f, f+1}: black pawns on ff all have rank < r? No — "no black pawn with rank > r" ⇔ max_rank_of_black_pawns_on_ff < r... wait rank > r means strictly in front? A black pawn at rank == r on adjacent file: can it capture the passer later? The passer moves to r+1, r+2...; black pawn at adjacent file rank r can capture onto file f at rank r+... black pawn at (f+1, r) attacks (f, r-1) — moving down, it can't catch a pawn moving up. Right: black pawn at adjacent file rank r is behind relative to passer. So strictly rank > r blocks. So need max black rank per file (the "backmost" for black = highest rank index? For black, pawns move toward rank 0; the least advanced black pawn is at the highest rank index). We need: exists black pawn on file ff with rank > r ⇒ not passed. That is max_rank_black[ff] > r ⇒ not passed. So maintain for black per file: max rank index (backmost pawn) — hmm but actually we need existence of ANY pawn with rank > r; the max rank is the best indicator: if max_rank_black[ff] > r then some pawn is at rank > r? Max rank is the largest; if largest > r yes. But if the pawn at max rank is... any pawn with rank > r exists iff max_rank > r. Yes since max is the largest. So maintain bp_max_rank[file] (max rank of black pawns on that file, i.e., least advanced). Similarly for black passers: wp_min_rank[file] (min rank = least advanced white pawn; any white pawn with rank < r exists iff min < r). OK this incremental bookkeeping adds bug surface. Given priorities, maybe simpler: eval scans pawn files directly. For each of 8 files, scan 8 squares: collect white pawn ranks and black pawn ranks on that file. From that per-file data compute doubled, isolated, passed. That's 64 board reads per eval — acceptable and much less error-prone. Also compute rook open files during piece scan of eval (already scanning pieces? If material+PST is incremental, eval doesn't scan pieces... rook open-file needs pawn counts per file — we get them from the pawn scan (counts per file). So eval = pawn scan (64 reads) + king safety (needs pawns in front of king — from pawn scan data per file we know counts but need rank proximity... king safety: pawns on files adjacent to king within distance 1-2 ranks. Could approximate: shield bonus for each own pawn on the 3 files around king with rank within 2. From pawn scan I have ranks per file per side — I can pick the min rank (for white, the closest pawn below/near king). Let me store per-file first-encounter info during scan: for each file, lowest white pawn rank and highest black pawn rank. Then shield: for files kf-1..kf+1, lowest white pawn rank <= king rank and king_rank - w_min_rank <= 2 → +bonus each. Something like that. OK final eval plan (white perspective, then negate for black): - Incremental: material, PST mg/eg, bishop pair? (needs counts of bishops — maintain bishop counts incrementally: wb_count, bb_count), phase. - Eval-time pawn scan per file: ranks of pawns per file/side. - Terms: - Doubled pawns: -12 mg / -20 eg per extra pawn on file (approx). - Isolated pawns: -14 mg / -18 eg per isolated pawn (no friendly pawn on adjacent file). - Passed pawns: bonus by rank: [0, 5, 10, 20, 35, 60, 100, 0] mg; eg bigger: [0, 10, 20, 35, 60, 100, 160, 0]? Something reasonable. - Rook open file: +20; semi-open: +8 (need rook locations: scan during pawn scan? Pieces not scanned... I need rook positions. Option: scan the back ranks? Rooks can be anywhere. Hmm — either scan whole board in eval (120 reads) or maintain piece lists. Alternatively track rook squares incrementally (rooks move rarely; maintain list of rook squares per side — on rook move/capture update; castling moves rook too; promotion to rook adds one). Rook list maintenance: 4 rooks max; store as set or list. On make: if moved piece is rook, update its entry (frm→to); if captured rook, remove; if promo rook... rare, add. Unmake: reverse. Use a dict {sq: color}? Or two small lists per side. Bug-prone but doable. Alternatively full scan: 120 board reads is honestly fine in Python? eval is called at every qsearch node — that's the hot path. 120 reads + logic ~ maybe 30-60 µs per eval. With ~50k nodes/sec target... eval dominating. Incremental material+PST avoids the main scan; the pawn scan (64) plus rook handling. Alternative: skip rook open-file term? It's a nice eval term. Or compute rook file term during pawn scan by checking specific squares? Rooks usually on back ranks or 7th. Hmm. Compromise: maintain per-side piece-square lists ONLY for rooks? Let me instead do a full board scan but keep it cheap: iterate over the 64 playable squares via precomputed index list `ALL_SQ64` (list of 64 board indices). For each occupied square accumulate terms. That's simpler and handles rooks, king safety, mobility uniformly. Cost: 64 iterations with a list index each. In Python maybe ~10-20µs. Given overall nps ~5-20k, fine. Simplicity → fewer bugs → "no error in runtime". I'll go full scan eval, no incremental material (or keep incremental? Full scan computes material too). Let me just do full-scan eval; it's clean and correct. Optimization: skip eval when in qsearch stand-pat? It's needed there too. Hmm, but "do your best algorithm" — incremental would be better but risks bugs. Middle ground: full scan but efficient (local variable caching, precomputed sq lists). I'll do full scan. Python speed will be modest (maybe 5-15k nps) but correctness first. Let me at least make the scan tight. Eval implementation sketch: ``` def evaluate(pos): board = pos.board side = pos.side # 1 or -1 mg = 0; eg = 0; phase = 0 wp_files = [[] for _ in range(8)] etc. ``` Actually to compute pawn structure efficiently: first pass over 64 squares collecting piece info into per-type lists? Two passes: pass 1 collect pawns per file (lists of ranks), count pieces, sum material+PST; pass 2 compute terms using collected data (rooks need file pawn counts — available from pass 1). King positions known from pos.wk/bk. King shield needs pawn ranks per file — from pass 1 (wp ranks per file list). Bishop pair from bishop counts. Passed pawn: need enemy pawn ranks on adjacent files — from pass 1 data (bp ranks per file). All doable in one pass + a small second loop over 8 files for pawn terms, plus loop over rook list for rook terms. Good. Mobility: for knights and bishops, count reachable squares — do in pass over collected piece lists. Knight: 8 offset lookups. Bishop: slide — 4 directions until blocked; count empty squares + 1 per capture maybe. This adds cost per bishop/knight but they're few. I'll include modest mobility: knight 4 per move... values: mobility_bonus = (count - baseline)? Simplified: +4 per knight target square beyond 4? Let me just add (n_moves * 4 - 16) for knights and similar for bishops — or simpler: count * 3. Eh. Simplify: knights: (moves*4 - 20)? If PST already covers centralization, mobility adds little; but it helps in qsearch. I'll include knight mobility = (moves-4)*4 mg/eg, bishop = (moves-6)*3. Rooks: skip (open file term covers). OK. King safety: mg only: shield = for each of files kf-1..kf+1: if friendly pawn exists with rank within [kr-2..kr] (for white; must be at or below king and within 2 ranks): +8 mg each (max 24). If no pawn on file (open file near king): -12. Only apply when phase > threshold? Tapered by phase anyway (mg component). Also penalty for open file adjacent to king? Keep the simple shield. Castling rights small bonus: +10 mg per side? King PST handles castled positions decently. Add small castling right bonus +8 mg per right — fine. Tempo: +10 for side to move (mg). Applied at end: score = (mg*phase + eg*(24-phase))//24, then add tempo*side... tempo typically added to the side to move; final return side * score. Let me: score_white_persp computed; return side==1? score : -score; plus 8 tempo. Fine. Insufficient material: if no pawns, no rooks/queens, and each side has <= 2 minor pieces total and... simple: no pawns and material diff small and minors <= 1 each → 0. Implement: if pawns==0 and queens==0 and rooks==0 and (wn+wb<=1) and (bn+nb<=1): return 0. Also KNvKN etc. fine. Draw by fifty move handled in search (halfmove >= 100 → 0). Repetition: check pos.hash in pos.rep_hist[:-1]... Implementation: rep_hist stores hash after each position; current position's hash at index? Let me define: rep list contains hashes of all positions since last irreversible move, INCLUDING current position. When making a move: push new hash. When unmaking: pop. At search node: check if current hash occurs earlier in rep list (count occurrences >= 1 in the part before current). I'll implement: `if pos.rep.count(pos.hash) >= 2` is O(n) each node... rep list is short (<= 100+depth). But count() scans whole list each node — expensive-ish. Optimize: only check the last N entries up to halfmove clock (repetition possible only within the halfmove window): scan from end, step 2 (same side to move), stop after halfmove entries. Implement small loop: ``` h = pos.hash; rh = pos.rep n = len(rh) - 1 # current at index -1? ``` Let me store rep as list where rep[-1] is current position hash (after make, we append; at node entry the position's hash is rep[-1]). Check if rep[i] == h for i in range(len(rep)-3, -1, -2) limited to halfmove window: repetitions must be spaced even moves. Count if any match → treat as draw (first repetition = draw in search — standard practice to score 0 on first repeat to avoid repetition blunders). Fine-tune: some engines only score draw on 2nd occurrence within search; first-repetition-draw is fine and common. Zobrist: need 64-bit random ints. Use Python's random with getrandbits(64), seeded deterministically (seed constant) for reproducibility. Keys: piece(12) x square(120) — only 64 squares used but index by 120 for speed (waste fine: 12*120 table). Side key, castling keys (16 combos or 4 individual — use 4 individual and XOR all applicable? Careful: incremental update with individual keys: when castling rights change from old to new, XOR keys for bits removed/added. Or table of 16 keys indexed by rights value: XOR out table[old], XOR in table[new] — simplest and safe. EP: 8 file keys (XOR when ep set; store ep as file). Use ep square+... I'll hash ep file when ep square set. Make move hash updates: - XOR piece from frm, XOR piece from to (or XOR captured), etc. Handle promo: XOR pawn out of frm, XOR promo piece into to. EP: XOR captured pawn at cap square. Castle: XOR rook frm/to. Side key XOR. Castling: XOR zc[old], zc[new]. EP: if old ep set XOR zep[old_file]; if new set XOR zep[new_file]. Now, move generation details (mailbox): Offsets: ``` N, S, NE, NW, SE, SW, E, W = 10, -10, 11, 9, -9, -11, 1, -1 KNIGHT = (8, -8, 12, -12, 19, -19, 21, -21) KING = (10, -10, 11, 9, -9, -11, 1, -1) BISHOP_DIRS = (11, 9, -9, -11) ROOK_DIRS = (10, -10, 1, -1) ``` Squares: A1=21...H8=98. Rank of sq: (sq // 10) - 2 (0..7). File: (sq % 10) - 1 (0..7). White pawn: push to sq+10; if empty: if rank == 6 (7th rank) → promotions (Q,R,B,N — generate 4, order Q first); elif rank == 1 (2nd) and sq+20 empty → double push flag. Captures sq+9, sq+11 if enemy piece (promos if to last rank). EP: if pos.ep and (sq+9 == ep or sq+11 == ep) and target square... ep square is empty; capture allowed if ep set and pawn attacks it: generate ep capture. For ep we need pos.ep square; generate if pawn_diag == ep. Black pawn: mirror with -10 etc. Castling generation (white): rights & WK: squares 26 (f1), 27 (g1) empty; not in check at 25 (e1); 26, 27 not attacked by black → move king 25→27 flag CASTLE. Rights & WQ: 24 (d1), 23 (c1), 22 (b1) empty; 25, 24, 23 not attacked → 25→23. Note: b1 (22) needs empty but not un-attacked. Black: e8=95, etc. I need is_square_attacked(sq, attacker_side): - Pawn: if attacker is white: white pawns attack sq from sq-9 and sq-11 (a white pawn on sq-11 attacks sq? White pawn at p attacks p+9 and p+11. So attacker squares are sq-9, sq-11. Check board[sq-9]==WP etc. with offboard check (board sentinel 100 ≠ 1 so equality fails naturally — nice: board[sq-9] == 1 works even off-board since sentinel is 100). - Knight: any of 8 offsets board[sq+o] == N*side. - King: 8 offsets. - Sliders: for each bishop dir: step until non-empty; if piece == B or Q of attacker → attacked. Rook dirs similarly R/Q. Return bool. Move ordering: score each move: - TT move: 1,000,000 - Captures: MVV-LVA: victim value * 10 - attacker value + 900,000 base... Standard: score = 1_000_000 + victim*100 - attacker. Promotions add promo value. EP counts pawn victim. - Killers: 900,000 (two per ply, killer1 > killer2). - History: counter heuristic: history[side][frm][to] += depth*depth on cutoff, capped. Score = history value (max maybe 800,000 cap). Order by sorting list of (score, move) descending. Sorting all moves each node costs; fine in Python. Killers indexed by ply; history dict or list [side][from*?]. Use dict keyed by (side, frm, to)? List of 2*120*120 ints = 28800 entries — fine: history = [0]*(2*120*120), index side_idx*120*120... side 1→0, -1→1: idx = (0 if side==1 else 1)*14400 + frm*120 + to. Fine. TT: dict hash → (depth, flag, score, move). Python dict of int64 keys. Size unbounded → could grow large in long sessions; add simple management: if len > N (e.g., 2_000_000) clear. Mate score adjustments: when storing, if score > MATE_BOUND: score += ply (make it node-relative? Convention: store mate scores as distance from current node). Standard: score stored = score + ply if score > MATE_BOUND... Let me define MATE = 100000; mate scores in search are MATE - ply_from_root. When storing to TT: if score >= MATE - 1000: store score + ply (so it's "mate in n from this node"); if score <= -(MATE-1000): store score - ply. When probing: if stored >= MATE-1000: score -= ply; if <= -(MATE-1000): score += ply. I'll do this carefully. Flags: 0 exact, 1 lower (score >= beta, fail-high), 2 upper (score <= alpha). Aspiration windows: start depth 1 full window; for depth >= 2: delta = 25? window = previous score ± 40, widen by *2 (or +40/ +80...) on fail. On fail-low or fail-high, re-search full window. Implementation: ``` score = search_root(depth 1) for depth in 2..max: alpha = score - 40; beta = score + 40 while True: score = root_search(depth, alpha, beta) if stop: break if score <= alpha: alpha = score - window_widen (or set alpha = score - 40*2^k; widen: window *= 2; alpha = score - window) elif score >= beta: beta = score + window else: break ``` Root search: I can just call negamax with ply 0 at root with a special root move list for PV extraction? Simpler: implement search() generally and call with ply=0, then extract PV from TT (follow best moves, guarding against cycles and depth limits). PV from TT is standard; add safety: max PV length 64, verify each move is legal in current position by making + legality test; stop on illegal/cycle. Also I can track a triangular PV table... simpler: TT-based PV with legality verification. But TT entries might be missing/overwritten — acceptable. Alternatively store PV via a "pv line" collection using recursion return — implementing triangular arrays in Python with lists is fine but adds complexity. TT-PV is fine with legality checks. Also print info each iteration: depth, score (cp or mate), nodes, nps, time, pv, maybe hashfull. Convert score: if abs(score) >= MATE-1000: mate_in = (MATE - abs(score) + 1)//2... define: if score > MATE-1000: plies = MATE - score; mate moves = (plies + 1)//2 → "mate X" positive; if negative similar. Let me define MATE = 100000 and in search return -(MATE - ply) for being mated at ply; MATE - ply for delivering mate at ply... Actually standard: at a node where side to move is checkmated: return -MATE + ply. So a score of MATE - ply at root means root side mates in (MATE-score+1)//2 moves. mate distance in moves = (MATE - score + 1)//2 when score > 0; when score < 0: (MATE + score + 1)//2? Score = -(MATE - ply) = -MATE + ply → mated in ply plies → moves = (ply+1)//2 = (MATE + score + 1)//2. I'll compute carefully: ``` if score >= MATE - 1000: n = (MATE - score + 1) // 2; s = f"mate {n}" elif score <= -(MATE - 1000): n = (-(MATE + score) + 1) // 2 ... ``` Let me just: plies = MATE - abs(score) if score > 0 → hmm. Let me define score conventions: mate scores = ±(MATE - ply). For score > 0: ply = MATE - score, moves = (ply + 1)//2. For score < 0: ply = MATE + score, moves = (ply + 1)//2, print mate -moves. I'll write helper. Node counting & nps: count qsearch nodes too. time.time() for timing; use time.perf_counter. Stop conditions: engine.stop_flag checked every 2048 nodes → raise SearchAbort. In iterative loop catch it, keep last completed depth's best move (also if root best move updated at current partial depth, some engines use best-so-far from completed iterations only — safe: keep best from completed iteration; but if depth 1 not completed... depth 1 completes almost instantly; if even depth 1 aborted (super short time), fall back to first legal move. Ensure we always have a legal move: at root, if no legal moves, output bestmove 0000? UCI GUI shouldn't ask in that case; to be robust: print "bestmove 0000". Hmm, some engines output any; I'll output 0000 with a guard.) Time management inside search: check every 2048 nodes: if time exceeded hard limit → abort. Between iterations: if elapsed >= soft limit → stop deepening. Node limit: abort when nodes >= limit (check in same place). Depth limit: loop bound. Mate search: if `go mate N`: stop when found mate score with moves <= N... and it should prove mate; approximation: if score >= MATE-1000 and (MATE - score + 1)//2 <= N → stop. Also if mated... keep searching maybe; simple. `go infinite`: no soft/hard limits; run to max depth (say 99) or until stop; abort on stop. `stop` command: sets flag; search aborts and prints bestmove. In threaded design: the search thread itself prints bestmove at end (whether aborted or finished). Main thread just sets flags. Need care to not print bestmove twice or when a new `go` started. I'll structure: Engine class with: - pos (Position) - stop_event (threading.Event), searching flag, ponder flag - search thread target: runs `think()` which: sets up limits from stored go params; runs ID loop; at end (if not suppressed) prints bestmove. If pondering: after search completes, wait for ponderhit/stop? If ponder search runs to depth limit... ponder typically until ponderhit. Implement: in ponder mode, ignore time limits (like infinite); when `ponderhit` received: set ponder flag false and set stop_time to now + allocated time (so search stops soon); when search thread finishes with ponder→normal transition... Actually flow: search thread runs ponder search with no limits (max depth high). Main thread on ponderhit: clear ponder flag, set deadline = now + remaining. Search thread checks deadline (now active) → aborts → prints bestmove. On `stop` during ponder: set stop → abort → print bestmove. On `position` during ponder: stop without bestmove (set a flag `suppress_bestmove`). Edge cases: `go ponder` position is the position after expected opponent reply... GUI handles. Our engine just searches current position. I think that's solid. Also handle `go` with no time (e.g., `go` alone): default movetime maybe 1000? UCI says infinite if nothing? Spec: if no param, search infinite? Actually UCI: "go" with no arguments — engine should probably search infinite or reasonable default. I'll default to movetime 1000... Hmm, safer: treat as infinite? That would hang GUIs expecting a move. Better default: use 5 seconds movetime? If GUI sends bare `go` it's usually testing. I'll default to infinite=False, movetime=5000? Hmm. I'll pick: default depth 6 if nothing? Let me just default movetime = 1000 — reasonable. Actually bare `go` per spec means "search infinite"? Let me recall the UCI spec: "go ... all parameters are optional" — if none given... The spec doesn't define; engines typically search a fixed depth or use infinite. I'll choose a sane default: movetime 1000. Document in `info string`? Not needed. Perft command for validation: `perft ` prints nodes and time. Also `divide`? perft is enough; maybe include both... perft only, plus `eval` debug prints eval. And `d` prints board+fen. Position.copy(): new Position sharing nothing mutable: board copy, rep list copy, undo stack fresh empty, scalars copied. Note: game history rep list — for search we want rep list to include game history; copying is correct (search pops back to initial state's entry). Careful: rep list contains initial position hash too (position after `position` command setup should have its hash appended... Let me define: Position.rep is list of hashes of positions since last irreversible, with rep[-1] == current hash after any make; and the base position (from FEN) also has its hash appended at setup. Then repetition check scans rep[:-1] step... positions alternate side; hashes with same side to move appear at indices of same parity from the end. Check: current index len-1; candidates len-3, len-5... but bounded by halfmove count. I'll implement: ``` def is_repetition(pos): h = pos.hash rh = pos.rep # current hash is rh[-1] (maintained) cnt = 0 limit = pos.halfmove # can't repeat older than halfmove clock i = len(rh) - 3 steps = 0 while i >= 0 and steps < limit: if rh[i] == h: return True i -= 2; steps += 2? ``` Each candidate is 2 half-moves back → steps increments by 2, compare against halfmove. Simpler: iterate i from len-3 down to max(0, len-1-halfmove), step -2. Eh: since each make increments halfmove (and resets on pawn move/capture), repetition can only involve entries within the last `halfmove` positions. positions list length from base is roughly halfmove+1 entries... roughly aligned. I'll just scan step -2 up to `halfmove` steps and also cap iterations at, say, 64 for safety. Minor imprecision OK. Wait — rep list also should not cross irreversible moves; but halfmove resets to 0 on pawn/capture and rep list isn't truncated. Scanning bounded by halfmove handles it approximately. Good enough (standard). Make/unmake details: ``` def make(self, move): frm = move & 127; to = (move >> 7) & 127; promo = (move >> 14) & 7; flag = (move >> 17) & 7 piece = board[frm]; captured = board[to] store undo = (move, piece, captured, self.castling, self.ep, self.halfmove, self.hash) update hash: remove piece at frm, remove captured at to, add piece (promo or piece) at to... if flag == EP: captured pawn at to -10*side (side=1: to-10; side=-1: to+10); remove from board and hash. if promo: board[to] = promo*side; else board[to] = piece board[frm] = 0 castling rights: self.castling &= mask[frm] & mask[to] (mask table: default 15, e1 clears W rights... mask[25] removes WK|WQ (bits), mask[21] (a1) removes WQ, mask[28] (h1) removes WK; mask[95] removes BK|BQ, mask[91] removes BQ, mask[98] removes BK.) Hash: XOR zcastle[old] then zcastle[new] — or since rights only decrease, XOR individual keys; use 16-table: XOR zc[old] ^ zc[new] handles both directions for unmake too. ep: new_ep = to if flag == DOUBLE else 0. hash: XOR zep[file(old_ep)] if old set; XOR zep[file(new_ep)] if new set. halfmove: 0 if pawn move or capture or promo else +1. kings: if piece == WK: wk = to (after castle also rook move: if flag == CASTLE: move rook: for white king to 27 (g1): rook 28→26; to 23 (c1): rook 21→24; black: to 97: rook 98→96; to 93: rook 91→94. Update board + hash for rook.) side = -side; hash ^= zside append hash to rep return undo (push to undo stack) ``` Unmake: pop undo, reverse everything: board[frm] = piece; board[to] = captured (0 for promo — captured stored is actual board[to] which is 0 for promo... wait if promotion captures, captured is the captured piece; if promo without capture, captured 0; board[to] = captured correct). EP flag: restore captured pawn at cap square: board[to -/+ 10] = -side_pawn — careful with side restored. Castle: move rook back. Restore castling, ep, halfmove, hash; pop rep; side = -side; kings restore (if piece is king → king square = frm). Edge: EP capture: board[to] was 0 (captured var = 0); restore pawn at ep-victim square. Need side of mover: at unmake time, side has been flipped; mover = -self.side after flip... In make I flip side at end. In unmake: first restore side: self.side = -self.side; then mover = self.side. Good. Null move make/unmake: separate functions: flip side, save ep (clear it, hash update), push undo (ep, hash), rep append? For repetition, null move shouldn't count for repetition... appending hash of null position is fine but two nulls in a row are prevented. Some engines don't append for null. I'll append and pop symmetric — safe. Hmm, but the position after null (same pieces, side flipped, ep cleared) could coincidentally match an earlier real position — that IS a repetition-relevant position actually (side to move with same arrangement). It's fine. Also for null: halfmove — increment? Keep halfmove unchanged (or increment; minor). Leave unchanged and don't push to rep? If left unchanged, rep list grows without halfmove bound alignment... I'll just handle null with its own simple make/unmake and append to rep; halfmove +1? Repetition bound uses halfmove as scan cap — if I don't increment halfmove, scan cap may be short — harmless. I'll increment halfmove? A null move isn't a real move; changing halfmove slightly affects fifty-move draws in search — negligible. Simplest: leave halfmove as is, append hash to rep, pop on unmake. Fine. Now legality check in search: after make, `if is_square_attacked(king_of_mover, side_to_move_now)` → illegal. king_of_mover: mover = -pos.side (after make). Track king squares in Position: pos.wk, pos.bk. mover side s = -pos.side; king = wk if s==1 else bk; attacked by pos.side. Castle legality handled at generation (through-squares attacked), plus post-make check covers destination... destination attack also checked at generation (g1 attacked check). Then post-make check is redundant but harmless. Move generation function returns list of pseudo-legal move ints. In-check evasion generation: full gen + filter works (no need for specialized evasion gen for correctness; performance slightly worse in check nodes; but check extension means we rarely need speed there). For qsearch when in check: generate all moves, filter legal, no stand-pat. Search function pseudo: ``` def negamax(pos, depth, alpha, beta, ply, can_null=True): nodes += 1 (counter global) if nodes & 2047 == 0: check limits → raise abort if ply > 0: if repetition or halfmove >= 100 or insufficient (check cheap?) → return 0 mate-distance pruning if depth <= 0: return qsearch(alpha, beta, ply) in_check = attacked(king, -side) if in_check: depth += 1 # TT probe alpha_orig = alpha tt_move = 0 entry = tt.get(pos.hash) if entry: edepth, eflag, escore, emove = entry tt_move = emove if edepth >= depth and ply > 0: escore = adjust mate score by ply if eflag == EXACT: return escore if eflag == LOWER and escore >= beta: return escore if eflag == UPPER and escore <= alpha: return escore # null move if can_null and not in_check and depth >= 3 and static has non-pawn material for side (track piece counts? compute quickly: use pos counts... I'll maintain counts of non-pawn material per side incrementally: npm_white, npm_black (excluding king). Also useful for insufficient material. Maintain in make/unmake.) R = 3 if depth > 6 else 2 make_null; score = -negamax(depth-1-R, -beta, -beta+1, ply+1, False); unmake_null if score >= beta: if score >= MATE - 1000: score = beta # avoid returning unproven mate return score # static eval for pruning (compute once) — only when not in check and depth low moves = generate(pos) order with tt_move, killers, history, MVV-LVA legal_count = 0; best_score = -INF; best_move = 0 fut_margin = (150, 250, 400, 600) if depth <= 4 else big for idx, move in enumerate(ordered): make(move) if illegal: unmake; continue legal_count += 1 gives_check = attacked(opp king, side) (side = mover) is_quiet = not capture and not promo # futility prune if is_quiet and depth <= 3 and not in_check and not gives_check and legal_count > 1: if static_eval + fut_margin[depth] <= alpha: unmake; continue # LMR new_depth = depth - 1 if depth >= 3 and legal_count > 3 and is_quiet and not in_check and not gives_check: r = 1 if legal_count < 12 else (2 if depth >= 5 and legal_count < 24 else ...) actually: r = 1 + (legal_count > 12) + (depth > 8)? keep simple: r = 1 if legal_count <= 12 else 2; also deeper for late: if depth >= 8 and legal_count > 20: r = 3? modest. score = -negamax(depth-1-r, -(alpha+1), -alpha, ply+1, True) if score > alpha: # re-search score = -negamax(new_depth, -(alpha+1), -alpha, ply+1) else: # PVS if legal_count == 1: score = -negamax(new_depth, -beta, -alpha, ply+1, True) else: score = -negamax(new_depth, -(alpha+1), -alpha, ply+1, True) if score > alpha and score < beta: score = -negamax(new_depth, -beta, -alpha, ply+1) unmake if score > best_score: best_score = score; best_move = move if score > alpha: alpha = score if alpha >= beta: if quiet: killers update; history update break if legal_count == 0: return (-MATE + ply) if in_check else 0 store TT: flag by alpha_orig/beta/best_score return best_score ``` Wait — with the abort exception raised mid-move (after make, before unmake), the position is corrupted — but we search on a copy, so fine. Also node counter increments and stop checks inside negamax and qsearch. Note: the "if nodes & 2047 == 0" check must be robust — use a global counter list or Engine attribute. Also careful: LMR re-search logic — after reduced search returns score > alpha, we re-search at full depth with same window; combined with PVS — fine. One more subtlety: futility pruning before making? I check gives_check after make — costs one attack query per quiet move at shallow depth. Acceptable. Actually simpler variant: compute gives_check only when needed (depth<=3 for futility or for LMR decision). Attack query is ~ a few µs... hmm that could double node cost. Alternative: use "is capture or promo" only for LMR eligibility (common) and for futility use condition without gives_check but require move_count > 1 and not in check and abs(alpha) small... Risk: pruning a quiet checking move that could matter — futility margins sized so it rarely matters at shallow depth. Many engines do futility without gives_check knowledge (they use "move_count based" pruning). To keep fine-tuning robust, I'll include the gives_check test but only when candidate for pruning (depth <= 3 and quiet and eval+margin <= alpha) — that query only happens for would-be-pruned moves, so cost is bounded. Same for LMR: only test gives_check when LMR candidate and... LMR candidates are many. Common engines don't exclude checks from LMR strongly; they use in_check and gives_check. Cost concern... I'll include gives_check check only when reduction candidate: `if quiet and legal_count > 3 and depth >= 3`: gives_check = attacked(...) once per candidate. It's one is_attacked call per quiet move — meh, acceptable. Actually is_attacked is our heaviest util; called for legality already once per move (after make). Two per move... Let me reduce: for LMR, skip the gives_check test (reduce anyway; re-search fixes misevaluations). For futility (depth <= 3), do the gives_check test only if static+fut_margin <= alpha (rare-ish at cutoff-free nodes... at futility-prunable nodes it's per quiet move). OK — but I also already compute legality attack per move. To reduce legality cost: note legality check tests if mover's king attacked; gives_check tests opponent king attacked — two separate calls, unavoidable. Accept. Hmm wait, actually there's a classical optimization: only test legality when the moved piece is the king, or the move is a potential pin (from frm along line through to king)... too complex; skip. qsearch: ``` def qsearch(alpha, beta, ply): nodes += 1; periodic stop check in_check = attacked(king, -side) if in_check: moves = gen_all; best = -INF for move ordered: make; if illegal skip; score = -qsearch(-beta, -alpha, ply+1); unmake if no legal: return -MATE + ply return best with alpha-beta stand = eval(pos) * side (evaluate returns white-persp; convert) if stand >= beta: return stand if stand > alpha: alpha = stand moves = gen_captures + queen promos order MVV-LVA for move: delta prune: if not promo and stand + val(captured) + 200 <= alpha: continue (skip ep? ep captures pawn 100 — fine include) make; if illegal: unmake continue score = -qsearch(-beta, -alpha, ply+1); unmake if score > best: best = score if score > alpha: alpha = score if alpha >= beta: break return best (or stand if no moves — initialize best = stand) ``` Note: qsearch could recurse deep in pathological positions; cap: if ply >= MAX_PLY-1: return eval. Add. Also draw checks in qsearch? Repetition within qsearch is rare (captures only); fifty-move: halfmove check cheap — add halfmove >= 100 → 0? in check handling... minor; add at top: if halfmove >= 100 return 0. OK. Move ordering scores — implement `score_moves(pos, moves, tt_move, ply)`: ``` for each move: decode frm,to,promo,flag if move == tt_move: score = 10_000_000 elif promo: score = 9_000_000 + promo_val (queen promos high even non-capture) elif capture (board[to] != 0 or flag==EP): MVV-LVA: victim = abs(board[to]) or pawn; score = 8_000_000 + victim*100 - attacker? Actually classic: victim*10 + (6 - attacker). Use values: victim = VAL[victim_type]*10 - attacker_type? Use piece values: score = 8_000_000 + vval*16 - aval. fine. else quiet: if move == killer1[ply]: 7_000_000 elif killer2: 6_990_000 else: history value (cap at 6_900_000? scale: history capped 1_000_000? Keep below killers: cap 500_000.) ``` Killer storage: killers = [[0,0] for _ in range(MAX_PLY+64)] — ply can exceed MAX_PLY with extensions? Cap ply in search: if ply >= MAX_PLY: return eval. Preallocate list of lists sized 256. History aging: on new search (each `go`), optionally halve; keep simple: reset per search? Reset loses learning within game but avoids overflow; scale down by halving at each new `go` — nice touch. Overflow guard: when updating, cap at 1_000_000: `if h > 800_000: halve all` — simpler: cap individual values. TT move validity: hash collision could give an illegal move → when ordering, tt_move is matched by exact int equality against generated moves; if not in the list, no effect. When making TT move — we only make generated moves. Safe. But in PV extraction we make TT moves directly — verify legality there (make + check). Good. PV extraction: ``` def get_pv(pos, max_len=30): pv = []; seen = set() p = pos (work on a copy) while len < max_len: e = tt.get(p.hash) if not e or not e[3]: break move = e[3] if move in seen-hash? guard cycles by hash set make move on copy; if illegal → break (check attack) — but make already done; test and break without recording. pv.append(move); also stop if hash repeats (set of hashes) return pv ``` Careful: making a move then detecting illegality leaves copy corrupted — but we discard the copy after; we only break. We record the move before verifying? Verify: make; if attacked(king) → break (don't append); else append. Good. UCI move formatting: from-to squares in algebraic + promotion letter lowercase. Parse UCI moves in `position ... moves e2e4`: find matching legal move (generate, compare frm/to/promo). Robust: try exact match; if not found, try ignoring... If move not found (illegal input), per robustness, ignore it? Better to apply nothing and keep position consistent — GUIs only send legal moves. If not found, break parsing (stop applying further moves). Log nothing to stdout except maybe info string... keep silent or print "info string ignored move X". I'll ignore rest silently (avoid corrupting). FEN output for `d`: implement `to_fen()`. FEN input robustness: parse fields; defaults if missing (e.g., ep '-' , halfmove 0, fullmove 1). Castling string parse: KQkq subset; validate letters; ignore unknown. Now: UCI `position fen <6 fields possibly> moves ...` — fen may contain spaces; parse until 'moves' token. Also handle 'position fen moves', 'position startpos moves ...'. Engine info: name "PyChess" or something like "PythonChessEngine"... call it "Cobra" hmm. Let me name it "Pythia"? cute. I'll go with name: "PyChess 1.0" — simple, with author. Threading details: ``` class Engine: def __init__(self): self.pos = Position(start_fen) self.tt = {} self.stop = threading.Event() self.search_thread = None self.ponder = False ... def uci_go(self, args): if self.search_thread and self.search_thread.is_alive(): (per spec GUI shouldn't; robustly: stop and wait briefly) self.stop.set(); join(timeout=2) parse params into self.limits self.stop.clear() self.suppress = False t = threading.Thread(target=self.think, daemon=True) self.search_thread = t; t.start() def think(self): try: run ID loop, print info lines, finally print bestmove unless suppressed except Exception as e: print bestmove of last known or 0000? To guarantee "no error at runtime", wrap: on unexpected exception, still print a bestmove (first legal move) to keep protocol alive, and print info string error to stderr? UCI stdout should stay clean-ish; send error to stderr. ``` Handling `quit`: set stop, and exit main loop (join briefly). `isready` → readyok (always, even during search — main thread handles). `ucinewgame`: stop search, clear TT, reset position, killers, history. `ponderhit`: if pondering: ponder=False; set deadline (based on time params if provided in the original go — for ponder, GUI sends wtime/btime too in go ponder? Typically yes, `go ponder wtime btime ...` includes times of the pondering side? Actually the times correspond to... GUIs send time params in ponder go as they'd apply. On ponderhit they might resend? No — ponderhit alone. The time params sent with go ponder apply after ponderhit. I'll capture them at go and compute deadline at ponderhit.) Also `stop` during ponder → abort → print bestmove. Position update during search: if `position` received while searching (spec violation), we stop search with suppress=True, wait for thread end, then apply. Implement via: set suppress flag then stop event; join; then apply position. Careful: think() prints bestmove in finally unless suppress. Race: `position` handler sets suppress then stop. think's finally checks suppress. Since think is in another thread, order: main sets suppress (before stop) — but think may be between search end and print... Use a lock around "finish printing" — simplest: engine.print_lock; in think: with lock: if not suppress: print bestmove. In position handler: with lock: suppress=True; stop.set(). Hmm, if think already acquired lock and printing... it's fine: position waits for lock, then sets suppress — but think already printed bestmove. GUI sent position while engine searching — it's a spec violation anyway; we did our best. Also join to ensure the thread isn't using pos while we mutate it. Actually the search runs on its own copy! Design: think() copies self.pos at start (with lock) and searches the copy — so main thread can even mutate self.pos safely without stopping search. But rep list copy... Position.copy copies everything mutable. Then position handler doesn't need to stop the search at all — but spec-wise engine must stop on position? UCI spec: "if the engine receives a position command during search it should stop"? Actually the GUI won't send it during search except when pondering (position while pondering means ponder missed → engine stops silently). For pondering correctness: on `position` while pondering: suppress bestmove and stop search. I'll implement: if ponder or searching: suppress=True; stop.set(); join(1.0); then apply position. For `ucinewgame`: stop, clear TT. Alright — the go parsing: ``` params: depth, nodes, mate, movetime, wtime, btime, winc, binc, infinite, ponder limit object: depth_limit (default MAX_DEPTH=64), node_limit (None), deadline_hard (None), soft_deadline (None), mate_in (None), infinite bool, ponder bool ``` Time allocation (non-ponder, our clock): time = wtime if side==1 else btime; inc similarly. If time is None → default movetime 1000. If time given: allocated = time/30 + inc*0.7? plus minimum floor 30ms. soft = start + allocated; hard = start + min(time*0.4, max(allocated*4, 200))? Ensure hard <= time - 100 (buffer) if time > 200. Let me define: ``` if movetime given: soft = hard = start + movetime - 10 (min 10) elif clock given: alloc = time // 30 + (inc * 3) // 4 alloc = max(alloc, 20) cap = time // 8? hmm for very short time... soft = start + alloc hard_limit = max(alloc, 50) ... define hard = start + min(max(alloc*4, 100), time*0.4)... guard time*0.4 < time - 50: if time < 150: hard = start + max(10, time - 50)?? getting fiddly. ``` Let me write it cleanly: ``` if clock: base = time / 30.0 + inc * 0.5 if base < 20: base = 20 if base > time / 5.0 and time > 0: base = time / 5.0 # safety cap... but time/5 could be tiny soft_ms = int(base) hard_ms = int(min(base * 4.0, time * 0.4)) if time > 250 else max(10, int(time * 0.5)) ensure hard_ms >= soft_ms (if base*4 > time*0.4, hard smaller than soft? min picks time*0.4 possibly < base*4; and if time/5 cap applied, base*4 vs time*0.4: base <= time/5 → base*4 <= 0.8*time > 0.4*time → min = 0.4*time ≥ base? 0.4 time vs base ≤ 0.2 time → hard ≥ soft ✓. ``` fine. Search stop check: `if self.nodes & 1023 == 0: if stop.is_set() or (deadline and now >= deadline): raise Abort`. Hard deadline; soft deadline only decides whether to start a new iteration. Node limit: `if node_limit and nodes >= node_limit: raise`. Also depth-1 completes even if time over? If movetime 10ms, depth 1 might take longer — we abort and use best from depth... If aborted during depth 1, we have best_move possibly None → fallback: run quick depth-1 no-prune? We guarantee fallback: track best move whenever alpha improves at root... With PVS at root implemented as negamax(ply=0), we don't directly see root moves. Hmm — root handling: I'd like a dedicated root search to (a) get best move reliably even on abort mid-iteration, (b) maybe track move scores. Many engines track "best move at root so far" from the root move loop even if iteration incomplete. Let me implement root search as its own function (search_root) that loops root moves explicitly — gives: best_move always set after first legal move searched (even partial iteration), aspiration handling at root, root move ordering (previous best first, then scores from previous iteration for ordering — sort root moves by previous iteration scores). Root move scores: dict move→score from last completed iteration; use for ordering next iteration. That's a nice fine-tuned touch. search_root(depth, alpha, beta): ``` moves = legal root moves (generate, filter by make/unmake legality — pre-filter to legal list) order: prev_best first, then by prev scores, captures MVV... for i, move: make on pos copy... wait root search operates on search position PVS: first move full [alpha,beta]; others null-window then re-search track best; if best > alpha: alpha = best; best_move = move (update immediately — even if iteration aborts later, best_move is valid) if alpha >= beta: break (won't happen at root with aspiration? beta cutoff at root possible with aspiration — fine) return best score, best_move ``` Root also handles: if only one legal move and clock-based search: could return quickly (fine-tune: if len==1 and not infinite/depth, do minimal search and play immediately). Nice touch, include: if one legal move and time control (not infinite/depth/mate), set best and skip deep search (or search depth 2 for safety and stop). Also fine-tune: don't start a new iteration if elapsed > soft*0.5? Standard: continue while elapsed < soft. Also predict: if elapsed * 2 > remaining-to-hard... Simple: start new depth only if elapsed < soft. Easy-move / stability heuristics: skip (complexity). Internal iterative reductions/deepening: skip (TT move mostly covers). More search fine-tuning to mention: - Razoring: when depth <= 2, not in check, static eval + margin < alpha → drop to qsearch? That's basically reverse futility... Reverse futility pruning (static null move): if depth <= 6 and not in check and eval - 90*depth >= beta → return eval (when non-PV). Add — cheap and effective. Careful with mate scores: if beta >= MATE-1000 skip. - I'll include reverse futility pruning (RFP) and futility move pruning, NMP, LMR, PVS, check extension, TT, killers, history, aspiration. That's a legitimately fine-tuned policy set. Also SEE for capture pruning? Skipping SEE — MVV-LVA + delta pruning in qsearch. Could add simple SEE for qsearch pruning (stop searching losing captures): a basic SEE implementation via recursive swap algorithm... adds bug risk. Many "qsearch prunes losing captures with SEE". Without SEE, qsearch explores all captures — slower but correct. I'll skip SEE (note in comments). Hmm "write your best algorithm" — SEE would help strength... Let me consider including a simple static exchange evaluation for pruning obviously-losing captures in qsearch: SEE(board, to_sq) via swap-off list. Implementation of SEE is moderately tricky (x-ray updates etc.). A simplified SEE without x-rays: find least valuable attacker of `to` for each side alternately... needs attacker-finding function (find least valuable piece of side attacking sq). That's another is_attacked variant per piece type — doable: for side, check pawns first, then knights, bishops, rooks, queen, king attacking sq; return square of least valuable. Then classic SEE recursion: ``` def see(pos, to_sq, side, gain list): swap algorithm: attackers... ``` Standard swap-off SEE: ``` def see(board, frm, to, side_to): gain = [val(board[to])] attacker side alternates d = 0 from_list = [frm] side = -side_to... ``` This gets intricate; risk of subtle bugs causing wrong pruning (pruning a good move is dangerous — could weaken play; SEE errors of "too low" prune good captures → tactical blindness; but errors rarely crash). "No error in runtime" mainly = no crashes. Still, I'll skip SEE to be safe and lean on MVV-LVA + delta pruning. Decision: skip SEE. Also add "late move pruning (LMP)" for quiets at shallow depth: if legal_count > 4 + depth*depth and quiet and not in check and not gives_check → skip at depth <= 3. Include modestly? It interacts with futility... I'll include a soft version: prune quiets after `3 + depth*depth` moves searched at depth <= 3, with the gives_check guard... The gives_check call per pruned-candidate move — bounded. Hmm, keep both futility and LMP? They overlap; I'll implement futility (with margin table) and a hard cap LMP at low depth. Fine. Also delta pruning margin: 200. Include queen promotions in qsearch gen (non-capture promos too? Usually queen promos included; underpromotions excluded). Include capture-promos naturally (captures include all promo captures? In qsearch generate captures + any queen promotion (even quiet). I'll generate captures (including capture-promotions of all kinds? keep all promos? Only queen). Keep: all captures (any promo) + quiet queen promos. Promotion generation in main gen: Q, R, B, N (4 moves). MVV-LVA scoring handles capture promos; quiet promos get promo bonus score. Now, is_attacked implementation (hot path) — write tight: ``` def attacked(self, sq, side): b = self.board if side == 1: # pawns if b[sq - 9] == 1 or b[sq - 11] == 1: return True (white pawn attacks upward, so pawn at sq-9/sq-11 attacks sq) — need white pawn located at sq-9: pawn at p attacks p+9 and p+11. p = sq-9 → attacks sq ✓. knights: for o in KNIGHT: if b[sq + o] == 2: return True king: for o in KING_OFFS: if b[sq + o] == 6: return True diag: for d in (11, 9, -9, -11): s = sq + d; while b[s] == 0: s += d; p = b[s]; if p == 3 or p == 5: return True ortho: for d in (1, -1, 10, -10): s = sq + d; while b[s] == 0: s += d; p = b[s]; if p == 4 or p == 5: return True else mirrored with negatives. return False ``` Note: while loop terminates at off-board sentinel 100 (not 0) ✓. Negative piece values break too (p != 0, then check p in (3,5)). For black: p in (-3, -5) etc. Board indices: sq-11 could be < 0 → b[negative] wraps in Python (negative indexing!) — sq >= 21 so sq-11 >= 10 ≥ 0 ✓. sq + 21 max 98+21 = 119 ✓ within list. Knight offsets: sq-21 ≥ 0 ✓ (21-21=0; b[0] is sentinel ✓). All good since playable squares are 21..98 interior; extremes: sq=21 (a1): 21-9=12 (sentinel row), 21-11=10, 21-21=0 ✓ in range. Eval PSTs: I'll use the "simplified evaluation function" tables (well-known, public domain from CPW). Plus endgame king table. Let me write them (white perspective, a1 at index 0? The classic tables are written from white's perspective with first row = rank 8. I'll define tables as 8x8 lists with row 0 = rank 8 (visual layout), then map: for white piece at rank r (0-based from rank1), file f: table[7 - r][f]. Let me precompute lookup: PST[piece][sq120]. Table values (Michniewski): Pawn: ``` 0, 0, 0, 0, 0, 0, 0, 0, 50, 50, 50, 50, 50, 50, 50, 50, 10, 10, 20, 30, 30, 20, 10, 10, 5, 5, 10, 25, 25, 10, 5, 5, 0, 0, 0, 20, 20, 0, 0, 0, 5, -5,-10, 0, 0,-10, -5, 5, 5, 10, 10,-20,-20, 10, 10, 5, 0, 0, 0, 0, 0, 0, 0, 0 ``` Knight: ``` -50,-40,-30,-30,-30,-30,-40,-50, -40,-20, 0, 0, 0, 0,-20,-40, -30, 0, 10, 15, 15, 10, 0,-30, -30, 5, 15, 20, 20, 15, 5,-30, -30, 0, 15, 20, 20, 15, 0,-30, -30, 5, 10, 15, 15, 10, 5,-30, -40,-20, 0, 5, 5, 0,-20,-40, -50,-40,-30,-30,-30,-30,-40,-50 ``` Bishop: ``` -20,-10,-10,-10,-10,-10,-10,-20, -10, 0, 0, 0, 0, 0, 0,-10, -10, 0, 5, 10, 10, 5, 0,-10, -10, 5, 5, 10, 10, 5, 5,-10, -10, 0, 10, 10, 10, 10, 0,-10, -10, 10, 10, 10, 10, 10, 10,-10, -10, 5, 0, 0, 0, 0, 5,-10, -20,-10,-10,-10,-10,-10,-10,-20 ``` Rook: ``` 0, 0, 0, 0, 0, 0, 0, 0, 5, 10, 10, 10, 10, 10, 10, 5, -5, 0, 0, 0, 0, 0, 0, -5, -5, 0, 0, 0, 0, 0, 0, -5, -5, 0, 0, 0, 0, 0, 0, -5, -5, 0, 0, 0, 0, 0, 0, -5, -5, 0, 0, 0, 0, 0, 0, -5, 0, 0, 0, 5, 5, 0, 0, 0 ``` Queen: ``` -20,-10,-10, -5, -5,-10,-10,-20, -10, 0, 0, 0, 0, 0, 0,-10, -10, 0, 5, 5, 5, 5, 0,-10, -5, 0, 5, 5, 5, 5, 0, -5, 0, 0, 5, 5, 5, 5, 0, -5, -10, 5, 5, 5, 5, 5, 0,-10, -10, 0, 5, 0, 0, 0, 0,-10, -20,-10,-10, -5, -5,-10,-10,-20 ``` King mg: ``` -30,-40,-40,-50,-50,-40,-40,-30, -30,-40,-40,-50,-50,-40,-40,-30, -30,-40,-40,-50,-50,-40,-40,-30, -30,-40,-40,-50,-50,-40,-40,-30, -20,-30,-30,-40,-40,-30,-30,-20, -10,-20,-20,-20,-20,-20,-20,-10, 20, 20, 0, 0, 0, 0, 20, 20, 20, 30, 10, 0, 0, 10, 30, 20 ``` King eg: ``` -50,-40,-30,-20,-20,-30,-40,-50, -30,-20,-10, 0, 0,-10,-20,-30, -30,-10, 20, 30, 30, 20,-10,-30, -30,-10, 30, 40, 40, 30,-10,-30, -30,-10, 30, 40, 40, 30,-10,-30, -30,-10, 20, 30, 30, 20,-10,-30, -30,-30, 0, 0, 0, 0,-30,-30, -50,-30,-30,-30,-30,-30,-30,-50 ``` For pawn eg, same table works (mostly mg); the simplified function uses single pawn table. With tapered eval, I can use pawn mg = pawn eg same table, or small eg adjustments. I'll use same for p/n/b/r/q (single table) and two king tables. Phase weights: N=1,B=1,R=2,Q=4; max 24. Material values: P=100, N=320, B=330, R=500, Q=900. Additions: - Bishop pair: +30 mg / +45 eg? common ~+30. I'll do +25 mg, +40 eg? hmm keep +30/+40. - Rook open +25 mg, +15 eg? I'll do open: +20 mg +10 eg; semi: +8/+5. - 7th rank rook? skip. - Passed pawn bonuses by relative rank (from own side): ranks 2..7 → [0, 10, 20, 35, 60, 100] mg-ish and eg [0, 20, 35, 60, 100, 150]? Plus king proximity in endgame would be nice but complex — passed eg table big enough. Fine. - Isolated: -12 mg, -15 eg? values -10/-15. - Doubled: -10 mg, -20 eg? I'll do -8/-15 per extra. - Connected/protected pawns? skip. - Tempo: +12. King attack terms? skip (shield only). OK. Insufficient material: implemented in eval: if no pawns and npm both sides small... conditions: no pawns, no queens, no rooks, minors: white minors <= 1 and black minors <= 1 → 0. Also KNN vs K etc. — treat as 0? KNN can't force mate; fine to 0. Also two knights vs lone king... covered (minors<=1 fails for 2 knights — draw still; add: if one side has 2 knights and other has nothing → 0? nice-to-have; skip, small eval edge is OK — actually KNN vs K should be a draw; without it engine might avoid trading into it or value it wrong slightly. minor. skip.) Fifty-move & repetition handled in search only (not eval). Now code structure (single file `engine.py`): ``` #!/usr/bin/env python3 """...docstring with usage...""" import sys, time, threading, random Constants: pieces, offsets, mailbox helpers, PSTs, zobrist init. class Position: __init__(from_fen or blank), set_fen, to_fen, copy, attacked, in_check, generate_moves (all / captures_only), make, unmake, make_null, unmake_null, is_repetition... class Engine: search + uci. helpers: move_to_uci, uci_to_move(index into generated legal moves), print_board. main(): uci_loop. ``` Let me be careful about details that commonly cause bugs: 1. Castling through/into check — handled in gen. 2. EP legality: after ep capture, discovered check along rank? The classic ep pin bug: white plays ep, both pawns leave rank 5, rook on rank 5 checks king. Our legality-by-make-and-test handles it ✓ (we test after making the move). 3. Castling rights when rook captured on home square — mask[to] handles ✓. When rook moves — mask[frm] ✓. King moves — mask ✓. Also promotion on corner squares (e.g., white pawn to a8 capturing rook) — mask[to] handles since a8=91 is a rook home? a8 is black queenside rook home; capturing there must clear black Q-side rights: mask[91] clears black Q-side ✓ if mask table includes all four corners for both colors appropriately: mask[sq] = 15 default; mask[25]=15-3=12? bits: WK=1, WQ=2, BK=4, BQ=8. mask[25] (e1) = ~3 & 15 = 12; mask[21] (a1) = 13; mask[28] (h1) = 14; mask[95] (e8) = 3; mask[91] (a8) = 7; mask[98] (h8) = 11. ✓ 4. EP square set even when no enemy pawn adjacent → hash includes ep → two transpositions differ. Minor; acceptable. (Could set ep only if capture possible; I'll add cheap check: when double push, set ep only if an enemy pawn sits on the two adjacent squares of the pushed pawn's origin-adjacent... ep capture possible if enemy pawn on files f±1 at the from-rank? White pushes e2e4: black pawn on d4 or f4 can capture. Check board[to-1]== -1 or board[to+1]==-1 → set ep. This also improves TT quality. Implement: cheap and correct? If no adjacent enemy pawn, no ep capture ever possible this ply → skipping ep square is semantically identical. ✓ include.) 5. Halfmove clock ≥ 100 draw — include check "if in checkmate it's still mate"? We test mate by no-legal-moves before draw scoring? Order in negamax: draw checks at node entry (ply>0): if halfmove >= 100 → return 0. If actually checkmated with halfmove 100, we'd wrongly return 0 — rare edge; FIDE says mate takes precedence. Handle: check in_check first and if in check, skip the fifty-move draw return? If in check with halfmove >= 100: proceed to move loop; if no legal moves → mate ✓. If legal moves exist → we should return 0 (fifty-move). Compromise: at node entry, if halfmove >= 100 and not in_check → return 0. If in check, continue (move loop decides mate or moves exist... but then we continue searching a drawn position — slight inefficiency, correct-ish result: if moves exist, position is a draw — we'd search it; then the score returned would be from the search, not 0 — technically the game ends there. Minor: mate detection takes precedence correctly; draw detection slightly late. Acceptable... Actually simpler and standard: most engines just return 0 on halfmove >= 100 without mate check. I'll do the in_check exception — it's cheap (in_check computed anyway... at node entry before computing in_check, we'd need it early — compute in_check at entry once and reuse. Fine.) 6. Zobrist collision TT: standard risk, accepted. 7. Rep detection scanning: implement carefully. Now — generate_moves implementation. I'll write one function with a flag `captures_only=False`. White gen (side==1): loop over squares 21..98: ``` for sq in ALL_SQ: (precomputed list of 64 playable indices) p = b[sq] if p == 0 or p == OFF: continue if p > 0 (white piece): switch on p... ``` Since board holds signed ints, generate per side with sign check. Write two branches (white/black) for clarity and speed. Pawn (white): ``` to = sq + 10 if b[to] == 0: if rank(to) == 7 (i.e., to >= 91): append 4 promos else: append quiet push if rank(sq) == 1 (sq between 31..38): if b[sq+20]==0: append double (flag DOUBLE) for d in (9, 11): to = sq + d t = b[to] if t < 0 (enemy, includes actual pieces; off-board is 100 > 0 ✓ won't trigger): capture (+promos if last rank) elif to == self.ep and self.ep: ep capture flag EP ``` Wait — `t < 0` for enemy: black pieces are -1..-6 ✓, off-board 100, empty 0, own 1..6. ✓. EP: self.ep is a square index or 0; if to == ep → flag EP. Knight/King: offsets; target t: if t == 0 or (t < 0 if white): move. Sliders (B/R/Q): dirs; step; while b[s]==0: append move; s+=d; then if enemy: append capture. Castling (white): as described. Need `attacked` calls — only if rights exist and squares empty (cheap ordering: emptiness first). Black mirrored: pawn pushes -10, promo when to <= 28 (rank 0 → sq 21..28); rank(sq)==6 for double (sq 81..88); captures d in (-9, -11); ep same (to == ep). generate flags: NORMAL=0, EP=1, DOUBLE=2, CASTLE=3. Promo field: piece code 2,3,4,5 (N,B,R,Q) — generate order Q first for ordering? We sort anyway by score. promo encoding in move int: bits 14-16 (values 0-7 fine). Now scoring captures: victim = abs(b[to]) or 1 for EP; VAL[victim]... define VAL = [0,100,320,330,500,900,20000] indexed by abs(piece) (king 20000 never captured legally). MVV-LVA: score = 8_000_000 + VAL[victim]*10 - VAL[attacker]//10? Use simple: victim*10 - attacker (piece codes): score = 8_000_000 + vtype*10 - atype. QxP before PxQ? MVV-LVA orders QxP (victim pawn 1*10 - queen 5 = 5) vs PxQ (1*... victim queen 5*10 - pawn 1 = 49) → PxQ higher ✓. Promo scoring: base 8_500_000 + VAL[promo]? plus capture bonus. Simplify: ``` score = 0 if capture: score = 8_000_000 + vtype*10 - atype if promo: score += 7_500_000 + VAL[promo] → capture-promo ≈ 15.5M > tt? TT move must be top: give TT 20_000_000. Hmm capture-promo > TT? set TT = 30_000_000. Killers 6_000_000, history below. ``` Let me set: TT 32_000_000; promos 16_000_000 + VAL; captures 8_000_000 + mvvlva; killers 6_000_000/5_999_999; history min(hist, 5_000_000). Wait promos should outrank most captures: 16M > 8M+up to 90 ✓. History update: on quiet beta cutoff: h += depth*depth; cap at 5_000_000 (saturate; also global halving when any exceeds... just clamp). Killers per ply: list killer1[ply], killer2[ply]: on cutoff: if move != k1: k2=k1; k1=move. Move loop: I'll sort descending by score using `moves.sort(key=..., reverse=True)` with a parallel score list — build list of (score, move) tuples and sort. Tuple creation per node ~ fine. Now let me also handle "gives_check" — after make: `opp_king = pos.bk if mover was white...` compute: mover = -pos.side (pos.side already flipped). opp king = pos.wk if mover == -1 else pos.bk. gives_check = pos.attacked(opp_king, pos.side)? attacked by mover side = mover. pos.attacked(sq, by_side) — by_side = mover = -pos.side. Hmm wait: after make, pos.side = opponent of mover. mover = -pos.side. gives_check = pos.attacked(king_of(pos.side), -pos.side). king_of(pos.side) = wk if pos.side==1 else bk. ✓ Legality: pos.attacked(king_of_mover, pos.side) — king_of_mover = wk if mover==1 else bk; mover = -pos.side. Eval function — write it now mentally: ``` def evaluate(self): # in Position b = self.board mg = eg = 0 phase = 0 # piece scan wp_files = [[] for _ in range(8)]? Use two arrays of counts + advanced info: wpr = [[] for _ in range(8)] # ranks of white pawns per file bpr = [[] for _ in range(8)] wnp = bnp = 0 (knight counts) etc. rook sqs lists: wr = [], br = [] wbp = bbp = 0 (bishops) for sq in ALL_SQ: p = b[sq] if p == 0: continue if p > 0: (white) t = p (1..6) r = (sq-21)//10? rank = sq//10 - 2; file = sq%10 - 1 mg += VAL[t] + PST_MG[t][sq]; eg += VAL[t] + PST_EG[t][sq] phase += PHASE[t] if t == 1: wpr[file].append(rank) elif t == 4: wr.append(sq) (rook) elif t == 3: wbp += 1 elif ... else: mirrored with subtraction and rank mirror for PST: PST indexed by sq for white; for black piece at sq use PST at mirrored square: mirror_sq = sq ^ 7? For 120 board: mirroring rank: sq_m = 21 + (7 - rank)*10 + file... precompute MIRROR[sq] table (120). mg -= VAL + PST[mirror]. ``` I'll precompute PSTW_MG[t][sq] and PSTB_MG[t][sq] (black uses same table mirrored) — lookup directly: for black piece: mg -= (VAL[t] + PST_MG[t][MIRROR[sq]]). Precompute MIRROR once. Then pawn structure: ``` for f in range(8): wl = wpr[f] # ranks if len(wl) >= 2: doubled: mg -= 8*(len(wl)-1); eg -= 15*(len(wl)-1) same black. isolated check: wcount[f]==0 and neighbors counts ==0 → isolated pawns each penalized: if wl and not (wcount[f-1] or wcount[f+1]): mg -= 10*len(wl); eg -= 12*len(wl) (need count arrays: wcnt = [len(x) for x in wpr]) passed: for each white pawn rank r in wl: passed = True for ff in (f-1, f, f+1) within bounds: if any black pawn rank > r in bpr[ff]: passed = False wait — black pawns with rank > r? Black pawn "in front" of white pawn means larger rank index? White pawn at rank r (0..7, moving up). Black pawn blocks if on same file with rank > r, or adjacent files with rank >= r+1? Earlier analysis: strictly rank > r on f and f±1 blocks (adjacent at rank > r can capture as pawn advances; at rank == r adjacent can't). Hmm: black pawn adjacent file at rank r+1: white pawn pushes to r+1 → black pawn can capture it ✓ blocks. At rank r+2, white pawn reaches r+2, black captures ✓. So any black pawn at rank > r on f-1..f+1 blocks. Condition: passed iff max(bpr[ff] or -1) <= r for all ff in {f-1,f,f+1}? No wait: iff no black pawn with rank > r, i.e., max_black_rank[ff] <= r for ff in f-1..f+1... careful: same file black pawn at rank == r impossible. So passed iff for ff: bpr[ff] empty or max(bpr[ff]) <= r? A black pawn at rank < r on adjacent file is behind — doesn't block ✓. So passed = all(max(bpr[ff], default=-1) <= r). Hmm but black pawn at rank exactly r on adjacent file: max = r ≤ r → doesn't block ✓ correct (it's beside, moving down, can't catch). bonus by r: PASSED_MG = [0, 5, 10, 20, 35, 60, 100, 0] (rank 0 = rank1: pawn can't be on rank 1... white pawns on rank 0? never (they start rank 1... actually rank index 1 = 2nd rank). r ranges 1..6 for white passers realistically (7 = promotion done). Table index r: [0,5,10,20,35,60,100,0][r]... r=1 → 5, ..., r=6 (7th rank pawn) → 100. PASSED_EG = [0, 10, 20, 35, 60, 100, 160, 0]. black passed: rank mirrored: bp at rank r passed iff no white pawn with rank < r on ff: min(wpr[ff], default=8) >= r. ``` Rooks: ``` for sq in wr: f = file: wcnt=..., bcnt=... if wcnt[f] == 0: (semi-open for white) if bcnt[f] == 0: open: mg += 20; eg += 10 else: semi: mg += 8; eg += 5 ``` Bishop pair: if wbp >= 2: mg += 25; eg += 40. King shield (mg only, scaled by opponent queen presence? keep simple mg): ``` kf = file of wk, kr = rank of wk for ff in kf-1..kf+1: pawns = wpr[ff] ranks <= kr and kr - pr <= 2 → shield += 8 per pawn (count one per file: the closest below king) if no pawn on ff with rank < kr? open file near king penalty: if wcnt[ff] == 0 (or no pawn at/below): mg -= 12 Apply only in mg (tapered automatically). Also only if phase high? taper handles. ``` Careful: after castling kr = 0; pawns at ranks 1,2 on f/g file → kr - pr = 1,2 ≤ 2 ✓ shield counts. Black mirrored: kr = 7 - rank; pawns with rank >= kr and diff ≤ 2. Tempo: at end: score = ((mg * phase + eg * (24 - phase)) // 24) then add side bonus: return (score if self.side == 1 else -score) + TEMPO? Since we use negamax convention: eval from side-to-move perspective: `return (score if side==1 else -score) + 12`? Hmm adding tempo constant: score_stm = white_persp * side + 12. I'll do that. Phase max clamp: phase = min(phase, 24). Insufficient material check at top of eval: if no pawns (all wpr/bpr empty) and phase small...: condition: not any pawns and no rooks/queens and (wn+wb <= 1) and (bn+bb <= 1) → return 0 (before tempo). Track minor counts in scan. Also K vs K+2 minors etc. still evaluated normally ✓. Now search driver (think): ``` def think(self): pos = self.pos.copy() (under lock) limits = self.limits start = perf_counter() nodes = 0 (local counter object; use self or closure list — I'll use a mutable list or class attrs; since single search thread, fine) tt = self.tt clear killers/history for new search? keep history across (halved). root_moves = legal moves of pos (list) if not root_moves: print bestmove 0000; return if only 1 root move and clock: minimal search: set depth 2 search... just do the ID loop with max depth small? Or directly: run search depth 1-2 quickly then stop. Simple: if len(root_moves)==1 and not (infinite or depth given) → set deadline soon (search depth 1, then return). Implement: max_depth = min(max_depth, 2)? but if infinite/ponder, search on. I'll implement: if one legal move and time-based: after depth 2 completes, break. iterative deepening: for depth in 1..max_depth: aspiration loop as described → score, best_move (from root function) after each completed iteration (score not fail): record: self.best_move = best; root_scores for ordering print info: depth, seldepth? track max ply reached (seldepth). score, nodes, nps, time, pv (extract from tt with root pos copy) check stop: stop.is_set() → break if soft deadline passed → break if mate found within requested → break if score is mate and depth >= mate_distance... also stop early if found mate score? Optionally stop when mate found and depth > mate distance (searching deeper confirms) — do: if abs(score) >= MATE-1000 and (MATE - abs(score)) <= depth → break? Mate scores: score MATE-ply... ply from root. If score >= MATE-1000: mate_plies = MATE - score; if depth >= mate_plies + 2: can stop (found forced mate). Nice fine-tune, include cautiously: `if score > MATE - 1000 and depth >= MATE - score: break`? That risks stopping when a shorter mate exists... but we've proven mate in (MATE-score); deeper search may find shorter mate but it's fine to play it. Include for being mated? also fine. print bestmove (+ ponder move? UCI bestmove can include ponder move — optional; GUIs appreciate. I'll include ponder PV second move: "bestmove xxxx ponder yyyy" when available.) ``` Root search function (on pos copy, uses negamax). Also must handle aspiration failure loop with re-search. Root ordering across iterations: maintain root_move_order: list sorted by last iteration's scores (desc); scores dict move→score. At root, order: previous best first, then previous scores desc, then MVV-LVA. I'll just re-sort each iteration: primary = prev_score (default -inf → use MVV-LVA as tiebreak). Root implementation: ``` def _root_search(self, pos, depth, alpha, beta, root_moves): best_move = root_moves[0]; best = -INF for i, mv in ordered: pos.make(mv); (already legality-filtered root moves) if i == 0: score = -negamax(depth-1, -beta, -alpha, 1, ...) else: score = -negamax(depth-1, -(alpha+1), -alpha, 1, ...) if alpha < score < beta: score = -negamax(depth-1, -beta, -alpha, 1, ...) pos.unmake() if stop aborted → propagate exception (board copy corrupt OK since we abort entire search) if score > best: best = score; best_move = mv if score > alpha: alpha = score if alpha >= beta: break return best, best_move ``` Hmm — aspiration at root with fail: score <= alpha (fail low): need full-window re-search. The think loop handles: if score <= alpha → widen alpha (and re-search); if score >= beta → widen beta and re-search. During re-search, best_move from failed search may be garbage-ish but still legal; we keep last good best_move from previous iterations in self.best_move; only update on successful (in-window) iteration. Also update best_move even on partial iteration if score > best-so-far? If aborted mid-iteration, best from this iteration might be from a partial search with fail bounds — still a reasonable move (first move searched fully). Common practice: use move from current iteration if it completed at least first move search. My root function updates best_move as it goes; if abort happens, exception propagates — need best_move communicated. Store in self._iter_best before raising... Simpler: in think's ID loop, wrap root call in try; on Abort: keep self.best_move from previous completed iteration. But for very short time controls, iteration 1 may abort before completing — fallback: complete depth-1 quickly with plain alpha-beta (depth-1 = few nodes; even 10ms should cover; if movetime < 5ms, emergency: pick first legal move / highest MVV-LVA). Robustness: in think: before ID loop, set self.best_move = root_moves[0]... better: order root moves by MVV-LVA initially so fallback is a sensible capture. Then even if everything aborts, we have a legal bestmove ✓. Also, update self.best_move after each completed iteration, and also mid-iteration via a callback? To keep robust with aborts: root search writes self._current_best when it improves within an iteration; think keeps self.best_move = last completed; on abort, if current iteration depth > last completed depth, use self._current_best? That's what strong engines do (best-so-far). I'll do: root function sets self.best_move whenever best improves AND for iteration depth >= last_completed. Simpler approach used by many: best_move updated whenever a root move improves alpha, period. Risk: within aspiration fail-low re-search, "alpha improvement" meaningless (everything <= alpha)... but any move that raises alpha within fail-low window is still decent. Honestly, simplest robust: after each aspiration-stable result, set self.best_move. On abort, self.best_move holds last stable result; if none yet (depth 1 incomplete): fallback ordering first move. Depth-1 root search abort chance is tiny (only if movetime ~0). Acceptable. Plus I'll additionally set best_move after depth 1 completes even if later aborted. Fine — implement: in think, after successful root search (in-window), update self.best_move and self.root_scores. On Abort exception: break out, use whatever self.best_move is (initialized to ordered first move). Hmm wait — one more subtlety: aspiration fail-low with re-search: the re-search calls root again; fine. Also "seldepth": track maximum ply reached: pass via engine attribute updated in negamax when ply > seldepth. Cheap. Node counting: engine.nodes incremented in negamax/qsearch entry. Stop check: in negamax/qsearch: `self.nodes += 1; if not self.nodes & 2047: self._check_stop()` where _check_stop raises SearchAbort if stop_event set or hard deadline passed or node limit reached. Note: `not (nodes & 2047)` — nodes is multiple of 2048 → check. ✓ Qsearch ply cap: if ply >= 100: return eval. Negamax ply cap: if ply >= MAX_PLY (=128): return eval... also guard killers array size (make killers list length MAX_PLY+2, index ply; cap ply before access). Let me now also write perft (with make/unmake legality filtering): ``` def perft(pos, depth): if depth == 0: return 1; total=0 for mv in pos.generate(): pos.make(mv); if not pos.attacked(mover king, ...): total += perft(depth-1); pos.unmake() ``` Standard perft counts leaf nodes; legal move count. Should give correct values for startpos: 20, 400, 8902, 197281, 4865609 (depth 4 feasible in Python: ~200k makes... depth 4: 197k leaves + internal ~ fine, maybe 5-10 seconds; depth 5 ~ 4.8M leaves — too slow-ish but allowed optionally). I must double check unmake correctness for all flags. Also `to_fen` for `d` and hash consistency (used for repetition across `position` moves — game history from position command: set_fen computes hash and rep=[hash]; each applied move uses make() which appends hash ✓). Position history across `position ... moves`: apply make for each move (validated against generated pseudo-legal + legality filter; if mismatch, stop applying (robustness)). Note: applying make requires move legality test: after make, if mover king attacked → illegal → unmake and reject → break parsing. FEN parse: split fields; board layout from rank 8 down. Fields: pieces, side, castling, ep, halfmove, fullmove. Build board, set king squares, compute hash via compute_hash() (full compute, simpler than incremental for setup), rep = [hash]. Also compute counts... eval doesn't need counts (full scan). npm for null-move: need non-pawn material check per side — implement helper `has_non_pawn(side)`: scan? Cheap-ish: maintain counts incrementally? Eval scans anyway; for NMP check, do a quick scan over ALL_SQ (64) — done at nodes with depth>=3 — hmm that's a lot of scans. Maintain simple counts: I'll maintain `self.npm = [white_npm, black_npm]`? Incremental updates in make/unmake for captures/promos. Add counters: w_npm (sum of piece values N,B,R,Q white) and b_npm. Update in make: on capture of non-pawn (victim abs in 2..5): subtract from victim side npm; promo: promo piece adds npm (if promo != ... all promos are N/B/R/Q → add VAL), pawn removed doesn't affect npm. Unmake reverses. King never captured. This is easy to keep consistent. Include. MATE detection edge: if root has no legal moves (checkmate/stalemate at root): output "bestmove (none)" 0000. GUI shouldn't send; handle gracefully. TT clearing: on ucinewgame: tt.clear(); history halved or cleared; killers cleared. TT size management: if len(tt) > 4_000_000: tt.clear() at go start (or halve). Python dict of ~4M entries ≈ hundreds MB — too much; cap at 1_000_000 entries (entry ~ tuple 4 ints + key — ~200+ bytes → 200MB at 1M... hmm 1M entries maybe 150-250MB. Cap at 500k? Set TT_MAX = 1_000_000 and clear when exceeded (during search, check occasionally: if len > max: tt.clear()). I'll set 2_000_000? Memory: each dict entry: key int (28-32B) + value tuple (4 elements, small ints — tuple ~76B + refs) + hash table overhead... ~150B → 300MB at 2M. Use 1M cap → ~150MB. OK, TT_MAX = 1_000_000. Alright, also `info string` on startup optional. Print "id name", "id author", options none, "uciok". MultiPV: not required; ignore. `setoption name ... value ...`: if unknown, ignore silently (UCI says must ignore unknown options). `register`: ignore. `debug on/off`: ignore. Input reading: `for line in sys.stdin:` — note buffering: fine. Also strip whitespace, handle EOF (break → exit). Empty lines skip. Unknown commands: ignore silently (UCI: ignore unknown). One more: UCI requires "bestmove" printed after go even if stop before any search — covered by fallback. `go mate N`: set max_depth high, and stop condition: mate found (score >= MATE-1000 with distance ≤ N). Also if being mated? Just search normally otherwise; implement stop check: after each iteration: if mate_in_target and score >= MATE-1000 and (MATE - score + 1)//2 <= target → break. (And even if score indicates we're mated, keep searching for best defense.) `go depth N`: max_depth = N. `go nodes N`: node limit. `go infinite`: no deadlines, max depth 64? Use MAX_DEPTH = 128? Depth 128 never reached in Python; set 64 to bound killers etc. Use 64. Hmm ply can exceed 64 with extensions... killers sized 256; depth max 64; fine. MAX_PLY = 256? ply bounded by repetition/50-move mostly ≤ ~100 + qsearch depth... qsearch ply can grow long in capture chains — cap qsearch ply at 128, return eval. killers/ply arrays sized 300 to be safe. Search ply: extensions + up to... depth 64 + qsearch — total ply cap 250. I'll size arrays 320 and enforce `if ply >= 300: return eval` in both. Seldepth tracking: max ply visited. Now — the qsearch in-check evasion: generating ALL moves at check nodes in qsearch and recursing qsearch — could explode; standard engines do this ("in-check qsearch evasions"). Depth of check chains bounded by ply cap. OK. Delta pruning: skip when in check obviously (we're in the stand-pat branch, not in check). Also skip delta pruning when promo (extra material). Implement: gain = VAL[victim] + (VAL[promo] - 100 if promo else 0); if stand + gain + 200 <= alpha: skip. Also prune losing captures in qsearch via MVV-LVA? Without SEE, a common cheap trick: if victim value < attacker value and not promo and stand + victim + 200 < alpha... same as delta. Keep delta only. TT probe in qsearch: skip. Let me also consider: negamax TT cutoff conditions with aspiration and PV nodes — standard safe usage: only cut when node is not PV (beta... a node is PV if alpha < beta - 1). Common practice: allow TT cutoffs in non-PV nodes only: `if edepth >= depth and (beta - alpha) > 1:`? Many engines cut in PV too but with exact-flag only... I'll cut in non-PV nodes (any flag per rules), and in PV only on EXACT entries. Hmm, PV exact cutoffs can be wrong due to search-instability but they're "exact" scores — commonly used. To be safe & simple: only cutoff when non-PV (beta-alpha>1)? That loses TT benefit at PV nodes (few nodes anyway). Decision: cutoffs only in non-PV nodes; PV nodes still use tt_move for ordering. Slightly conservative, robust. Hmm — but at PV nodes with aspiration... fine. Also: don't take TT cutoff at ply==0 (root) — root handled separately anyway (root doesn't probe). One more standard trick: "TT depth adjustment" etc. skip. Now let me also double-check the negamax draw handling: node entry: ``` if ply > 0: if pos.halfmove >= 100 and not in_check_at_entry: return 0 if self._is_repetition(pos): return 0 # insufficient material quick check? cheap version using npm and pawn presence... needs pawn counts — skip here (eval handles 0 but search doesn't know draws... insufficient material positions get eval≈0 naturally; qsearch stand pat 0 — fine, no explicit draw needed.) ``` in_check_at_entry requires attacked call before move loop — we need in_check anyway for extension. Compute once at top: `in_check = pos.attacked(king_sq, -pos.side)`. Order: compute in_check; if halfmove >= 100 and not in_check → return 0. Mate-distance pruning: ``` alpha = max(alpha, ply - MATE) # being mated no worse than this? standard: alpha = -MATE + ply beta = min(beta, MATE - ply - 1) hmm sign conventions: mate score for side to move: MATE - ply_to_mate_from_root... The pruning: if alpha >= beta: return alpha. ``` Standard MDP: `mate_val = MATE - ply; if alpha < -mate_val: alpha = -mate_val; if beta > mate_val: beta = mate_val... wait careful. Score at node (side to move) if it mates: MATE - (ply + k). If it gets mated: -(MATE - (ply + k)). So best possible ≥ -(MATE - ply)? If checkmated right now: score = -MATE + ply = -(MATE - ply). So scores ≤ -(MATE - ply) impossible to be better than... The node's score is ≥ -(MATE - ply) (can't be mated faster than now) and ≤ MATE - (ply+1) (can't mate in 0 more plies). Prune: if alpha >= MATE - (ply+1)?? Let me just use known formulas: ``` alpha = max(alpha, -MATE + ply) beta = min(beta, MATE - ply) if alpha >= beta: return alpha ``` Hmm known implementation (from many engines): ``` mate_in = MATE - ply if alpha < -mate_in: alpha = -mate_in? ``` I've seen: `alpha = max(alpha, -MATE+ply); beta = min(beta, MATE-ply); if alpha >= beta: return alpha`. If we're possibly mated at this node, score = -MATE+ply exactly; so nothing better than -MATE+ply exists for the opponent's hopes... this raises alpha to at most what we can be forced into — it's sound. And beta lowered to MATE - ply: we can't deliver mate sooner than 1 ply from here... mate at this node? We deliver mate after our move → mate score MATE - (ply+1) ≤ MATE - ply. OK so beta = min(beta, MATE - ply - 1)? If beta = MATE - ply and true score = MATE - ply - 1 < beta — cutoff wouldn't trigger wrongly... a cutoff triggers when score >= beta; score max = MATE-ply-1 < MATE-ply = beta → no false cutoff. Safe either way. I'll use: ``` a = -MATE + ply; if alpha < a: alpha = a b = MATE - ply; if beta > b: beta = b if alpha >= beta: return alpha ``` Good. Zobrist key for position: compute at setup by iterating board. Piece index for zobrist: piece code -6..6 → map: Z[piece][sq] with piece index offset: use dict-free: ZOBRIST[(piece + 6) * 120 + sq]? piece+6 ∈ 0..12 (skip 6=empty). Flat list Z = [rand]*(13*120). Index: (p+6)*120 + sq. For black p=-1 → 5... fine. Castling zobrist: ZC[16]. EP: ZE[8] by file. Side: ZS. compute_hash: iterate ALL_SQ, add Z[p+6][sq] for nonzero pieces; XOR ZC[castling], ZS if side==1? Convention: XOR ZS when black to move? Either; pick: hash ^= ZS if side == -1? Let me: hash includes ZS when side == 1 (white to move). make(): side flips → hash ^= ZS ✓ (toggle). Consistent everywhere. EP hashing: only when ep square set: hash ^= ZE[file]. In make: if old ep: hash ^= ZE[old file]; if new ep: ^= ZE[new file]. Now let me also design `generate` returns; move int: frm = bits 0-6, to bits 7-13, promo bits 14-16 (value 0 or 2-5), flag bits 17-19. MAX move int ~ (98 | 98<<7 | 5<<14 | 3<<17) — fine. Helpers: ``` def move_from(m): return m & 127 def move_to(m): return (m >> 7) & 127 def move_promo(m): return (m >> 14) & 7 def move_flag(m): return (m >> 17) & 7 def make_move(f, t, promo=0, flag=0): return f | (t << 7) | (promo << 14) | (flag << 17) ``` UCI string: file/rank from square: sq_name(sq) = "abcdefgh"[sq % 10 - 1] + str(sq // 10 - 1). Move str: frm + to + promo letter ("nbrq"[promo-2]... promo codes: N=2,B=3,R=4,Q=5 → letter = "nbrq"[promo-2]? N=2 → 'n' index 0 ✓, B=3 → 'b' idx 1 ✓, R=4 → 'r' ✓, Q=5 → 'q' ✓.) Parsing uci move: generate legal moves; find move with matching frm,to,promo; return it (or None). Now, code size: this will be ~900-1100 lines. That's expected for this request. The user asked for a full engine — long answer justified. I should keep prose around it brief: a short intro, the code, short usage notes (how to run, test with GUI, perft validation). Let me now write the actual code carefully, double-checking as I go. Key correctness spots to verify as I write: 1. make(): promotion capture — board[to] = promo*side; captured = board[to] before overwrite ✓. Hash: remove piece at frm (pawn), remove captured at to, add promo piece at to. 2. EP make: captured pawn square = to - 10 if mover white (ep target is on rank 5 for white capture? White captures ep: ep square on rank 6 (indices 71-78)? White pawn on rank 5 (61..68) captures to rank 6 (71..78), victim black pawn on rank 5 at to-10 ✓. Black ep: victim at to+10.) 3. Castling make: white O-O: king 25→27, rook 28→26. O-O-O: king 25→23, rook 21→24. Black: O-O: 95→97, rook 98→96; O-O-O: 95→93, rook 91→94. Identify by `to` square. 4. King square update: if abs(piece)==6: king sq = to (castling: king to ✓; unmake: king sq = frm ✓). Also if a king is captured — impossible in legal search; but perft on illegal positions might — guard not needed. 5. unmake(): restore in reverse order. Undo record: (move, piece, captured, castling, ep, halfmove, hash, w_npm, b_npm?) — npm: instead of storing, reverse the delta: in unmake we know captured/promo → recompute npm adjustments mirror-image. Simpler: store previous npm values in undo tuple (2 ints) — dead simple and bug-free. Undo tuple: (move, piece, captured, castling_old, ep_old, halfmove_old, hash_old, wn_old, bn_old). Tuple of 9 — fine. Null move undo: separate stack or same with flag? Use separate small save in a local: in negamax we do make_null then unmake_null immediately around recursive call — no interleaving (no exception recovery needed mid-null since abort corrupts copy anyway). Implement make_null returns saved tuple (ep, hash) push? The rep list push/pop within make_null itself. halfmove: increment? leave. OK. 6. Rep list: make appends new hash; unmake pops. Null: appends new hash (side flipped, ep cleared); unmake pops ✓. But null moves make rep list contain a position where it's opponent's turn with same pieces — could cause false repetition match with a real earlier position where it was also opponent's turn and ep same → that IS a genuine repetition of position (same side to move, same pieces, same castling) — real! OK good. 7. Root copy for search: `Position.copy()` — copies board list, scalars, rep list (list()), undo stack empty (copy doesn't copy undo stack — searches always start from a settled position ✓). 8. Legality filter for `position` command: same as search: make → if pos.attacked(king_of_mover, pos.side) → unmake, invalid. Now let me think about `attacked()` sign details once more: ``` def attacked(self, sq, by): # by: 1 = white attackers, -1 = black b = self.board if by == 1: if b[sq - 9] == 1 or b[sq - 11] == 1: return True for o in KNIGHT_OFF: if b[sq + o] == 2: return True for o in KING_OFF: if b[sq + o] == 6: return True for d in (11, 9, -9, -11): s = sq + d while b[s] == 0: s += d p = b[s] if p == 3 or p == 5: return True for d in (10, -10, 1, -1): s = sq + d while b[s] == 0: s += d p = b[s] if p == 4 or p == 5: return True return False else: if b[sq + 9] == -1 or b[sq + 11] == -1: return True ... == -2 knights, -6 king, sliders -3/-5 diag, -4/-5 ortho. ``` Black pawn at p attacks p-9 and p-11 (down-left/right). Attacker of sq at p = sq+9 (pawn at sq+9 attacks (sq+9)-9 = sq ✓). While loops: b[s] == 0 loop; s can wander beyond board? Sliding from sq=21 dir -11: 21-11=10, b[10]=OFF(100)≠0 → stop ✓. From sq=98 dir +11: 109 < 120 ✓ OFF → stop. From sq=28, dir +1: 29 is OFF (col 9) ✓. Max index: sq 95 +11 = 106 < 120 ✓; sq 98 + 10 = 108 ✓; sq 98+11=109 ✓; sq 98+1=99 OFF. All within 0..119 ✓. Min: 21-11=10 ≥ 0 ✓; knight sq-21 = 0 ✓. OFF constant = 100. board list length 120. ALL_SQ: [21..28, 31..38, ..., 91..98]. Now, evaluate() — write with care about ranks: rank_of(sq) = sq // 10 - 2 → 0..7 (rank index 0 = rank 1). file_of(sq) = sq % 10 - 1. MIRROR[sq] for 120: 21 + (7 - (sq//10 - 2)) * 10 + (sq % 10) = let me compute: rank r, file f: mirrored = 21 + (7 - r) * 10 + f = 21 + 70 - 10r + f; sq = 21 + 10r + f → 10r + f = sq - 21. mirrored = 91 - 10r + f = 91 - (sq - 21 - f) + f = 112 - sq + 2f? Let me just compute via rank/file at init: `MIRROR = [0]*120; for sq in ALL_SQ: r = sq//10 - 2; f = sq%10 - 1; MIRROR[sq] = 21 + (7 - r)*10 + f`. Non-playable squares map to 0 (never used). PST building: ``` PST_MG = [None]*7; PST_EG = [None]*7 for t in 1..6: tbl = TABLES_MG[t] # 64 entries, row0 = rank8 (visual) arr = [0]*120 for sq in ALL_SQ: r = sq//10 - 2; f = sq%10 - 1 arr[sq] = tbl[(7 - r)*8 + f] PST_MG[t] = arr ``` For black: at eval, for piece p<0: sq_mirror = MIRROR[sq]; value = VAL[t] + PST_MG[t][sq_mirror]; score -= value. Eval loop (single pass, collecting): ``` wp = [[] for _ in range(8)] # ranks of white pawns per file bp = [[] for _ in range(8)] wr = []; br = [] wbi = bbi = 0 wn = bn = 0 (for insufficient) mg = eg = 0; phase = 0 for sq in ALL_SQ: p = b[sq] if p == 0: continue if p > 0: t = p v_m = VAL[t] + PST_MG[t][sq]; v_e = VAL[t] + PST_EG[t][sq] mg += v_m; eg += v_e; phase += PHASE_W[t] if t == 1: wp[sq % 10 - 1].append(sq // 10 - 2) elif t == 2: wn += 1 elif t == 3: wbi += 1 elif t == 4: wr.append(sq) else: t = -p m = MIRROR[sq] v_m = VAL[t] + PST_MG[t][m]; v_e = VAL[t] + PST_EG[t][m] mg -= v_m; eg -= v_e; phase += PHASE_W[t] if t == 1: bp[sq % 10 - 1].append(sq // 10 - 2) elif t == 2: bn += 1 elif t == 3: bbi += 1 elif t == 4: br.append(sq) ``` Then: ``` # insufficient material if not any pawn lists have entries and phase <= 2 (only minors: each side ≤ 1 minor... phase<=2 means total minors ≤2) → return 0 (from white persp → return side*0 + tempo? Draw → return 0 ignoring tempo? Return 0. But eval is from side-to-move persp; draw → 0 ✓.) Condition: phase <= 2 and no pawns → covers K vs K, KB vs K, KN vs K, KB vs KB, KN vs KN, K vs KN+... phase 2 = two minors total. KNN vs K = phase 2 ✓ draw → 0 ✓ good. But KN vs KN with pawns? "no pawns" required ✓. Hmm wait phase includes rooks=2, queens=4 → phase<=2 excludes them ✓. ``` ``` # pawn structure wcnt = [len(x) for x in wp]; bcnt = ... for f in range(8): wl = wp[f] if wl: # doubled if len(wl) > 1: mg -= 8 * (len(wl)-1); eg -= 15 * (len(wl)-1) # isolated if (f == 0 or not wcnt[f-1]) and (f == 7 or not wcnt[f+1]): mg -= 10*len(wl); eg -= 12*len(wl) # passed for r in wl: blocked = False for ff in (f-1, f, f+1): if 0 <= ff <= 7: for br_ in bp[ff]: if br_ > r: blocked = True; break if blocked: break if not blocked: mg += PASSED_MG[r]; eg += PASSED_EG[r] # black mirrored bl = bp[f] if bl: if len(bl) > 1: mg += 8*(len(bl)-1); eg += 15*(len(bl)-1) isolated: mg += 10*len(bl); eg += 12*len(bl) for r in bl: blocked = any white pawn with rank < r on ff in f-1..f+1: if not blocked: mg -= PASSED_MG[7-r]; eg -= PASSED_EG[7-r] ``` Rooks: ``` for sq in wr: f = sq % 10 - 1 if wcnt[f] == 0: if bcnt[f] == 0: mg += 20; eg += 10 else: mg += 8; eg += 5 for sq in br: f = ... if bcnt[f] == 0: if wcnt[f] == 0: mg -= 20; eg -= 10 else: mg -= 8; eg -= 5 ``` Bishop pair: wbi >= 2 → mg += 30, eg += 45; bbi >= 2 → -=. King shield (mg only): ``` kf = file of self.wk; kr = rank of self.wk for ff in (kf-1, kf, kf+1): if 0 <= ff <= 7: shielded = False for r in wp[ff]: if r <= kr and kr - r <= 2: shielded = True; break if shielded: mg += 10 else: mg -= 12 # black kf = file of self.bk; kr = rank of self.bk for ff ...: for r in bp[ff]: if r >= kr and r - kr <= 2: shielded if shielded: mg -= 10 else: mg += 12 ``` Wait — for white king at rank 0 (back rank) and pawn at rank 1 or 2: kr - r = 1 or 2 ✓. If king on rank 0 and no pawns in front → -12 per file. Also full open file toward king adds vulnerability — that's the -12. Reasonable. Note: this penalizes uncastled centralized king heavily (files around e1 without pawns → -36) — but PST handles much. OK. But hold on — shield check "r <= kr and kr - r <= 2": if pawns advanced beyond king (r > kr), no shield — fine (king behind pawn wall usually kr=0). OK. Tempo & scale: ``` if phase > 24: phase = 24 score = (mg * phase + eg * (24 - phase)) // 24 return (score if self.side == 1 else -score) + 12 ``` Hmm integer division with negative mg: Python floor division on negatives — (mg*phase + eg*(24-phase)) could be negative; //24 floors — slight asymmetry; acceptable. Or use int(.../24)? Float rounding; keep //. Note: eval returns score from side-to-move perspective; search uses it directly; qsearch stand-pat = eval ✓. Now, one important detail — the eval scan is ~64+ iterations with append calls for pawns etc. Building `wp = [[] for _ in range(8)]` allocates 8 lists each eval — fine. Now the search function, full: ``` def _negamax(self, pos, depth, alpha, beta, ply, can_null): self.nodes += 1 seldepth update if not (self.nodes & 2047): self._check_time() pos side stuff... if ply: # draw checks (fifty, repetition) if pos.halfmove >= 100 and not pos.in_check(): return 0 — in_check computed... order: compute in_check first. if self._is_rep(pos): return 0 # MDP v = -MATE + ply if alpha < v: alpha = v v = MATE - ply if beta > v: beta = v if alpha >= beta: return alpha in_check = pos.attacked(own king, -side) (own king = wk if side==1 else bk) if depth <= 0: return self._qsearch(pos, alpha, beta, ply) if in_check: depth += 1 # TT probe tt_move = 0 e = self.tt.get(pos.hash) if e is not None: ed, ef, es, em = e tt_move = em if ed >= depth and (beta - alpha) > 1 and ply > 0: if es >= MATE - 1000: es -= ply elif es <= -(MATE - 1000): es += ply if ef == 0: return es if ef == 1 and es >= beta: return es if ef == 2 and es <= alpha: return es # reverse futility (static null move) if not in_check and (beta - alpha) == 1 and depth <= 5: ev = pos.evaluate() (cost: an eval call at every non-PV shallow node — hmm; RFP triggers often; eval each such node... they're leaf-ish anyway. OK.) wait — but if ev - 100*depth >= beta and abs(beta) < MATE - 1000: return ev (or beta). Guard: if beta < MATE-1000... and also require non-pawn material? Not needed. Actually careful: RFP returns before move loop — standard. Include depth>=1... use margin 80*depth? Let me use 75*depth + 50? I'll use margin = 90 * depth. # static eval for futility (only if depth <= 3, not in check): compute lazily: se = pos.evaluate() — we may have already computed ev for RFP — reuse variable. # Null move pruning if can_null and not in_check and depth >= 3 and pos.npm(side) > 0 (side to move has non-pawn material — need "side to move" npm: pos.wnpm if side==1 else pos.bnpm; must be > ~ knight? require >= 300? use > 0? endgame K+pawns vs K: NMP disabled due to zugzwang — require npm >= 330? I'll require npm >= 300.) R = 3 if depth >= 6 else 2 pos.make_null(); score = -self._negamax(pos, depth - 1 - R, -beta, -beta + 1, ply + 1, False); pos.unmake_null() if score >= beta: if score > MATE - 1000: score = beta # don't return unproven mate scores return score # moves moves = pos.generate() scores ordering... legal = 0; best = -INF; best_move = 0; alpha0 = alpha for i, mv in ordered: pos.make(mv) if pos.attacked(mover king, pos.side): pos.unmake(); continue legal += 1 quiet = (captured == 0 and promo == 0) — captured value from make? make returns undo tuple; captured = undo[2]; I can check board pre-make... make returns undo; captured piece = undo[2] (board[to] before) but EP capture has captured==0 at to... For EP, captured stored is 0 but it IS a capture. Determine quiet via flag: quiet = (undo[2] == 0 and flag != EP and promo == 0). Let me compute is_cap = (undo[2] != 0) or (flag == 1). fine. # futility pruning if not in_check and se is not None and quiet and legal > 1 and depth <= 3 and abs(alpha) < MATE - 1000: gives_check = pos.attacked(opp king, mover) if not gives_check and se + FUT[depth] <= alpha: pos.unmake(); continue # also LMP at depth <= 2? skip — futility enough. # search with PVS + LMR if legal == 1: score = -self._negamax(pos, depth - 1, -beta, -alpha, ply + 1, True) else: r = 0 if quiet and legal > 3 and depth >= 3 and not in_check: r = 1 if legal <= 12 else 2 if depth >= 8 and legal > 20: r += 1? keep: r = 1 + (legal > 12) + (depth > 8 and legal > 18)? I'll write: r = 1; if legal > 12: r = 2; if depth >= 7 and legal > 20: r = 3 score = -self._negamax(pos, depth - 1 - r, -(alpha + 1), -alpha, ply + 1, True) if score > alpha and r: # re-search at full depth (null window) score = -self._negamax(pos, depth - 1, -(alpha + 1), -alpha, ply + 1, True) if score > alpha and score < beta: score = -self._negamax(pos, depth - 1, -beta, -alpha, ply + 1, True) pos.unmake() if score > best: best = score if score > alpha: alpha = score best_move = mv if alpha >= beta: if quiet: killers update (if k1 != mv: k2=k1; k1=mv) history[side][frm][to] += depth*depth (cap) break if legal == 0: return (-MATE + ply) if in_check else 0 # store TT store: flag = 1 (lower) if best >= beta else (2 upper if best <= alpha0 else 0 exact) adjusted = best; if adjusted > MATE-1000: adjusted += ply... wait store convention: store node-relative: if best >= MATE - 1000: stored = best + ply; elif best <= -(MATE - 1000): stored = best - ply. Probe adjusted inverse ✓ (probe did es -= ply for positive... let me recheck: stored = best + ply (mate distance from node = MATE - (ply_from_root... ) hmm: At node with ply p, score best = MATE - p_from_root_mate? Our scores: mate found at ply q (leaf mated): score at leaf = -MATE + q; propagates up: at ply p node, if subtree mates in k plies from node p: score = MATE - (p + k). "Node-relative" mate distance k: stored score should encode k: k = MATE - score - p → stored = MATE - k = score + p ✓. Probe: node at ply p2: es = stored - p2 ✓ (recovers MATE - k - p2). Negative: score = -(MATE - (p+k)) → stored = score - p; probe: es = stored + p ✓. Matches code above ✓. Also store only if depth >= e-depth of existing? Replacement: always replace (depth-preferred: if new depth >= old depth or old not present): implement: existing = tt.get; if not existing or existing[0] <= depth or random? Simple: replace if new depth >= existing depth. Keep first-searched deep entries. I'll do: replace if (old is None) or (depth >= old[0]). Hmm this keeps older deep entries even when position reassessed at same depth — same depth replace ✓. Fine. ``` Wait, one thing: `beta - alpha > 1` TT cutoff — at non-PV nodes. Aspiration windows at root create PV nodes at root children with narrow windows... fine. FUT margins: FUT = {1: 120, 2: 250, 3: 420}? I'll use list [0, 130, 260, 430, 0, 0]. se (static eval) computed when: `not in_check and depth <= 3` → se = pos.evaluate() else None. And RFP at depth <= 5 non-PV: also needs eval — compute ev once: if not in_check and (depth <= 3 or (depth <= 5 and non-PV)): se = pos.evaluate(). Then RFP uses se. Good — one eval max per node. RFP: `if se - 90*depth >= beta and beta > -(MATE-1000) and beta < MATE-1000: return se`? return beta? Returning se (fail-soft-ish) fine; guard mate-window. Hmm also RFP when in check excluded ✓ (se None when in_check since condition includes not in_check — ensure se computed only when not in_check). Note: RFP before TT probe or after? Order: TT probe first (cheap dict get) then RFP... RFP before null move ✓. Some do RFP before TT; whatever. I'll do TT probe, then RFP, then NMP. Edge: RFP at PV nodes? Condition (beta-alpha)>1 excludes PV... I applied RFP only non-PV ✓. Now qsearch: ``` def _qsearch(self, pos, alpha, beta, ply): self.nodes += 1 if not (self.nodes & 2047): self._check_time() if ply >= 300: return pos.evaluate() in_check = pos.attacked(own king, -side) if in_check: moves = pos.generate() order (TT move? no tt in qsearch; MVV-LVA + killers? simple MVV-LVA/quiet history — use same scorer with tt=0) best = -INF legal = 0 for mv: make; if illegal: unmake continue legal += 1 score = -self._qsearch(pos, -beta, -alpha, ply+1) unmake if score > best: best = score if score > alpha: alpha = score if alpha >= beta: break if legal == 0: return -MATE + ply return best se = pos.evaluate() if se >= beta: return se if se > alpha: alpha = se best = se moves = pos.generate_captures() order by MVV-LVA (+promo) for mv: # delta prune victim val = VAL[abs(undo captured)] or 100 for EP; promo bonus if se + gain + 200 <= alpha: continue — need undo info before make? compute from board: victim = abs(b[to]) or (EP → 1) — compute pre-make cheaply: decode move, victim = b[to]; vval = VAL[abs(victim)] if victim else (100 if flag==EP else 0); promo val add. make; if illegal: unmake; continue score = -qsearch... unmake; update best/alpha/beta return best ``` generate_captures: variant flag in generate(caps_only=True): pawn: only capture diagonals (+EP, + capture promos; quiet promotions excluded? include quiet queen promos — I said include; but caps_only... I'll include queen promotions in caps_only (they're tactical). Implementation detail: in caps_only mode: knights/kings/sliders only generate capture targets; pawns: captures + EP + (pushes to promo rank as queen promo only? Also capture-promos all? capture-promos are captures ✓ included; quiet push-promo queen only included). MVV-LVA in qsearch via the same score function (tt_move=0, no killers? include killers — killers exist per ply; cheap to include, fine). Now generate() full implementation: ``` def generate(self, caps_only=False): b = self.board; side = self.side; mvs = [] ep = self.ep if side == 1: for sq in ALL_SQ: p = b[sq] if p <= 0: continue if p == 1: to = sq + 10 if b[to] == 0: if to >= 91: # promotion rank (rank 7 index → 21+70=91..98) for pr in (5,4,3,2): mvs.append(mk(sq,to,pr)) elif not caps_only: mvs.append(mk(sq,to)) if sq <= 38 (31..38: rank index 1) and b[sq+20] == 0: mvs.append(mk(sq, sq+20, 0, 2)) for d in (9, 11): to = sq + d; t = b[to] if t < 0: if to >= 91: for pr in (5,4,3,2): mvs.append(mk(sq,to,pr)) else: mvs.append(mk(sq,to)) elif ep and to == ep: mvs.append(mk(sq,to,0,1)) elif p == 2: for o in KNIGHT_OFF: to = sq + o; t = b[to] if t <= 0 and t != OFF... wait t <= 0 includes OFF=100? No: OFF=100 > 0. t <= 0 → t is 0 or negative ✓ (own pieces 1..6 excluded, off-board 100 excluded ✓). but careful: `t <= 0` — 0 empty ✓, negatives enemy ✓. But also need `t != OFF`? OFF=100 not ≤ 0 ✓ fine. if caps_only: only t < 0 append mk(sq,to) elif p == 3 (bishop), 4 (rook), 5 (queen): dirs by type; loop: for d in DIRS[p]: to = sq + d while b[to] == 0: if not caps_only: append to += d if b[to] < 0: append capture elif p == 6: if not caps_only: king offsets (t <= 0) castling: if self.castling & 1 (WK): if b[26]==0 and b[27]==0 and not attacked(25, -1) and not attacked(26,-1) and not attacked(27,-1): mvs.append(mk(25,27,0,3)) if self.castling & 2 (WQ): if b[24]==0 and b[23]==0 and b[22]==0 and not attacked(25,-1) and not attacked(24,-1) and not attacked(23,-1): append mk(25,23,0,3) else: (black mirror) pawns: to = sq - 10; promo when to <= 28 (21..28); double from sq 81..88 → b[sq-20]; captures d in (-9, -11) with t > 0 and t != OFF → t > 0 and t < 7? b[to] in 1..6: condition `0 < t < 7`: careful OFF=100: 0 < 100 < 7 false ✓. knights: t >= 0 and t < 7 → 0..6 ✓ (OFF excluded since 100 not < 7 ✓). Wait t >= 0 includes empty and white ✓. sliders: while b[to] == 0; then if 0 < b[to] < 7: capture. king: e8=95: castling black: rights bits 4 (BK): b[96]==0 and b[97]==0 and not attacked(95,1), 96, 97 → mk(95,97,0,3); bit 8 (BQ): b[94]==0 and b[93]==0 and b[92]==0 and not attacked(95,1), 94, 93 → mk(95,93,0,3). return mvs ``` Wait black pawn promo rank: black pawn moving to rank 0 (indices 21..28): to <= 28 ✓ and to >= 21 (always). Double: black pawns start rank index 6 → squares 81..88: sq >= 81 and sq <= 88 → b[sq-20]==0 → append mk(sq, sq-20, 0, 2). White double: sq in 31..38: sq >= 31 and sq <= 38 → sq+20. EP for black: to == ep ✓ same. Now the caps_only promotions: quiet queen promo for white: `if b[to] == 0` branch with to >= 91 → generate only pr=5 in caps_only mode. And in caps_only, skip the double push and quiet non-promo pushes ✓ handled by elif not caps_only. Also note: in caps_only knight/king: `if t < 0` (white) capture only; king captures. ✓ Castling excluded in caps_only ✓ (inside `not caps_only`). Now make(): ``` def make(self, move): b = self.board frm = move & 127; to = (move >> 7) & 127; promo = (move >> 14) & 7; flag = (move >> 17) & 7 side = self.side piece = b[frm] captured = b[to] undo = (move, piece, captured, self.castling, self.ep, self.halfmove, self.hash, self.wnpm, self.bnpm) h = self.hash # remove piece from frm h ^= ZP[(piece + 6) * 120 + frm] hmm define ZP index: ZP[(piece+6)*120 + sq]? piece+6 for white pawn 1 → 7... black pawn -1 → 5, king white 6 → 12, black king -6 → 0. OK flat: Z = [rnd for _ in range(13*120)]; idx = (piece+6)*120 + sq. b[frm] = 0 if captured: h ^= Z[(captured + 6)*120 + to] # npm if side == 1: self.bnpm -= NVAL[abs(captured)]... define NPM_VAL = [0,0,320,330,500,900,0] for pawn/king zero? captured king? impossible; king has 0 npm ✓ safe. b[to] = 0? overwritten anyway if promo: newp = promo if side == 1 else -promo b[to] = newp h ^= Z[(newp + 6)*120 + to] if side == 1: self.wnpm += NPM_VAL[promo] else: self.bnpm += NPM_VAL[promo] else: b[to] = piece h ^= Z[(piece + 6)*120 + to] if flag == 1: # EP csq = to - 10 if side == 1 else to + 10 cp = b[csq] # enemy pawn (-1 if white moving? white EP captures black pawn = -1) b[csq] = 0 h ^= Z[(cp + 6)*120 + csq] captured = cp # for unmake info? undo already stored captured = 0 (b[to] was 0). unmake needs to know EP to restore: flag in move tells it. OK don't overwrite. elif flag == 3: # castle: move rook if to == 27: rf, rt = 28, 26 elif to == 23: rf, rt = 21, 24 elif to == 97: rf, rt = 98, 96 else: rf, rt = 91, 94 (to == 93) rp = b[rf]; b[rf] = 0; b[rt] = rp h ^= Z[(rp+6)*120 + rf] ^ Z[(rp+6)*120 + rt] # king if piece == 6: self.wk = to elif piece == -6: self.bk = to # castling rights old_c = self.castling new_c = old_c & CMASK[frm] & CMASK[to] if new_c != old_c: h ^= ZC[old_c] ^ ZC[new_c]; self.castling = new_c # ep old_ep = self.ep if old_ep: h ^= ZE[old_ep % 10 - 1] new_ep = 0 if flag == 2: # set only if enemy pawn adjacent (capture possible) if side == 1: if b[to - 1] == -1 or b[to + 1] == -1: new_ep = to - 10 else: if b[to - 1] == 1 or b[to + 1] == 1: new_ep = to + 10 hmm wait — adjacent enemy pawns that could capture are on the same rank as the pushed pawn's *landing* square? White pawn e2→e4: black pawns on d4/f4 capture to e3. b[to-1], b[to+1] with to=e4: d4, f4 ✓ correct. if new_ep: h ^= ZE[new_ep % 10 - 1] self.ep = new_ep # halfmove if piece == 1 or piece == -1 or captured != 0 or promo: self.halfmove = 0 else: self.halfmove += 1 # side & hash h ^= ZSIDE self.hash = h self.side = -side self.rep.append(h) self.undo.append(undo) return undo ``` Wait: EP capture sets captured var originally = b[to] = 0; halfmove check `captured != 0` misses EP → EP is a pawn move anyway (piece == ±1) → halfmove reset ✓. npm on promo: pawn removed... npm excludes pawns ✓ nothing to remove. Promo piece adds ✓. Captured piece npm subtract: `if side == 1: self.bnpm -= NPM_VAL[abs(captured)] else: self.wnpm -= ...` but abs(captured)==1 pawn → NPM_VAL[1] = 0 ✓; abs==6 king → 0 ✓ (never happens). unmake(): ``` def unmake(self): undo = self.undo.pop() move, piece, captured, castling, ep, halfmove, hash_, wnpm, bnpm = undo b = self.board self.side = -self.side # back to mover side = self.side frm = move & 127; to = (move >> 7) & 127; promo = (move >> 14) & 7; flag = (move >> 17) & 7 b[frm] = piece b[to] = captured if flag == 1: csq = to - 10 if side == 1 else to + 10 b[csq] = -1 if side == 1 else 1 elif flag == 3: if to == 27: b[28] = 4; b[26] = 0 elif to == 23: b[21] = 4; b[24] = 0 elif to == 97: b[98] = -4; b[96] = 0 else: b[91] = -4; b[94] = 0 if piece == 6: self.wk = frm elif piece == -6: self.bk = frm self.castling = castling; self.ep = ep; self.halfmove = halfmove; self.hash = hash_ self.wnpm = wnpm; self.bnpm = bnpm self.rep.pop() ``` Note EP restore: mover white → captured black pawn (-1) at to-10 ✓. null: ``` def make_null(self): undo = (self.ep, self.hash, self.halfmove) if self.ep: self.hash ^= ZE[self.ep % 10 - 1] self.ep = 0 self.side = -self.side self.hash ^= ZSIDE self.halfmove += 1? Hmm fifty-move: null isn't a real move — but incrementing keeps rep-bound logic consistent. I'll increment. self.rep.append(self.hash) self.undo.append(undo) # separate? undo stack shared with make — unmake pops and unpacks 9 elements; null undo has 3 → use a separate null stack or a tag. Use separate stack: self.nundo. But interleaving make/null: can make and null interleave on one stack? In search: make_null → recurse → unmake_null immediately; no interleaving with make across (make happens inside recursion after null — stack order: make...null...unmake_null...unmake — nested properly IF null pair is properly nested ✓ they are: unmake_null called right after the recursive call returns, before outer unmakes. So shared stack with tagged entries works: push ('n',) tuple vs 9-tuple; in unmake detect... but I made separate functions make_null/unmake_null that pop their own stack. Since properly nested, two stacks both behave LIFO correctly ✓. Use self.nundo list. def unmake_null(self): ep, hash_, halfmove = self.nundo.pop(); self.side = -self.side; self.ep = ep; self.hash = hash_; self.halfmove = halfmove; self.rep.pop() ``` Wait: hash_ stored is pre-null hash ✓ restore directly ✓ (and restore ep: stored pre-null ✓). side flips back ✓. Rep bound with null: rep.append fine. Hmm — one subtle thing: halfmove incremented during null → rep scan bound uses halfmove (larger) → fine. Now the repetition check: ``` def is_rep(self): rh = self.rep; h = self.hash # current position's hash is rh[-1]; look back every 2 plies n = len(rh) - 3 cnt = 0 limit = self.halfmove # can't repeat before halfmove window i = n steps = 0 while i >= 0 and steps < limit: if rh[i] == h: return True i -= 2 steps += 2 return False ``` Hmm — limit: entries i are `k` half-moves back where k = (len-1 - i). Each step back 2 = 2 half-moves. halfmove counts half-moves since last irreversible; repetition candidates must be within that ✓. Also cap scan to avoid pathological length: len bounded by game length; scanning up to 100 entries every node — costly! At nodes deep in search, rep list ~ game length + ply (could be 200+ entries) but limit=halfmove usually small in middlegame... opening positions have large halfmove? After `position startpos moves e2e4 ...` halfmove resets often. Long endgames: halfmove could be 60+ → 30 iterations per node — meaningful cost. Optimize: scan while steps < limit but limit also ≤ len. Accept ~ tens of iterations worst case; typical much less. Alternatively check only when halfmove >= 4 (repetition needs ≥ 4 half-moves): `if self.halfmove >= 4 and is_rep()` — saves calls in tactical trees. ✓ include that gate in negamax: `if pos.halfmove >= 4 and pos.is_rep(): return 0`. Note rep[-1] is current hash — appended in make ✓; at root (after copy), rep[-1] is the root hash ✓. Also: the fifty-move draw: `if pos.halfmove >= 100 and not in_check: return 0` — in_check computed before ✓ order: compute in_check first at node, then checks. But careful: negamax computes in_check AFTER draw checks in my earlier sketch? Let me restructure node entry: ``` in_check = pos.attacked(king(side), -side) # needed anyway if ply: if pos.halfmove >= 100 and not in_check: return 0 if pos.halfmove >= 4 and pos.is_rep(): return 0 MDP... ``` in_check computed at every node including depth<=0 → then qsearch also computes in_check — duplicate computation at the transition (negamax depth<=0 → qsearch recomputes). Minor waste; restructure: negamax: draw checks & MDP first (halfmove check without in_check... the "not in_check" for halfmove — hmm. FIDE: if 100th halfmove delivers mate, mate counts. Rare; simplify: `if pos.halfmove >= 100: return 0` even in check — tiny correctness edge, big simplicity. Actually let me keep mate priority with minimal cost: draw checks after computing in_check; duplicate in_check computation at qsearch entry only at depth-transition nodes — negligible. Final order in negamax: ``` nodes++, seldepth, stop check if ply >= MAX_PLY: return pos.evaluate() in_check = attacked if ply and (halfmove >= 100 and not in_check): return 0 if ply and halfmove >= 4 and is_rep(): return 0 if ply: MDP if depth <= 0: return qsearch(alpha, beta, ply) if in_check: depth += 1 TT probe... ``` Good. check_time raise: define class SearchAbort(Exception). _check_time: ``` if self.stop_event.is_set(): raise SearchAbort() if self.hard_deadline is not None and perf_counter() >= self.hard_deadline: raise SearchAbort() if self.node_limit is not None and self.nodes >= self.node_limit: raise SearchAbort() if len(self.tt) > TT_MAX: self.tt.clear() ``` TT clear mid-search loses PV... acceptable rare event. Actually clearing TT when bestmove depends... it only weakens future searches, not correctness. But also: clearing during aspiration could cause inconsistency — harmless. Root search & PV: also, for ponder output: second PV move. think() flow (runs in thread): ``` def think(self): try: self._think() except Exception: traceback to stderr; ensure bestmove printed: try: ensure some legal move as bestmove except: pass finally: if not self.suppress: print(f"bestmove {self.best_move_uci}", flush=True) ``` Careful: print bestmove exactly once. Set self.suppress default False at go; position/ucinewgame handlers may set True + stop before join. _think(): ``` with self.pos_lock: pos = self.pos.copy() limits... self.nodes = 0; self.seldepth = 0 killers reset (fresh per search), history halved. tt = self.tt # root legal moves root = [] for mv in pos.generate(): pos.make(mv) if not pos.attacked(king of mover, pos.side): root.append(mv) pos.unmake() if not root: self.best_move = 0; return order root initially by MVV-LVA desc self.best_move = root[0] # fallback if len(root) == 1 and time-based (not infinite/ponder/depth/mate given): after depth 2 stop → set self.force_stop_after_depth = 2? implement flag. start = perf_counter() max_depth = limits.depth or 64 prev_score = -INF? None scores = {} # move -> last score for depth in 1..max_depth: # aspiration if depth >= 4 and last_score is not None: delta = 30? window: alpha = last - 30; beta = last + 30 else: alpha = -INF; beta = INF (INF = MATE*2? use 1000000) while True: try: score, best = self._root_search(pos, depth, alpha, beta, root, scores) handle abort: raise to outer if score <= alpha: # fail low: widen alpha = max(-INF_BOUND, alpha - widen) where widen grows: widen *= 2 each fail? Track fail count: if alpha == -INF_BOUND: break (full window fail low — shouldn't happen: score would be true score ≥ ... with full window fail low impossible since best = max). Handle: if alpha <= -INF_BOUND: alpha = -INF? Let me: if alpha <= -MATE*2?? set alpha -= 200; if alpha < -100000: alpha = -100000 last? also last_score = score? no. elif score >= beta: beta = min(...beta + 200...) else: break # in window last_score = score self.best_move = best scores update: from root search — my _root_search returns only best score... to order root moves by per-move scores, _root_search should record scores per move (dict filled when moves searched with full window? With PVS, non-first moves get bounds... approximate: record score for moves that were searched; on aspiration re-search, re-record. Simplify: scores dict: for i==0 move: score exact; others: null-window result lower bound-ish. Fine for ordering. elapsed = perf_counter() - start print info: depth, seldepth, score (cp/mate), nodes, nps, time (ms), pv via TT. check stop conditions: if stop_event set: break (abort raised inside root search anyway → propagates) if mate target reached: break if limits.soft_deadline and elapsed >= soft: break if score >= MATE - 1000 and depth >= (MATE - score) + 1? hmm mate_plies = MATE - score; if depth >= mate_plies: break (mate proven to this depth... roughly). I'll include: if score > MATE - 1000 and depth >= MATE - score: break (also if score < -(MATE-1000) and we're being mated — keep searching for best defense; no break.) if single root move and forced flag: break after depth 2. ``` Abort handling: _root_search propagates SearchAbort; _think wraps the whole ID loop in try except SearchAbort: pass (keep last best). Structure: ``` try: for depth...: ... except SearchAbort: pass ``` Then final: print bestmove in caller with ponder. Get ponder: extract PV at root: pv = self._pv_line(pos_copy) — from TT using current position copy (root pos — but root pos object mutated by search... it ended settled? On abort mid-recursion, pos undo stack not unwound — pos corrupted! Use a fresh copy: copy pos again? pos was already a copy of engine.pos; after abort it's corrupt — make another copy from engine.pos under lock for PV extraction ✓. pv[0] should equal best_move (if TT has it); take ponder = pv[1] if pv and pv[0] == best else None. PV extraction: ``` def _pv_line(self, pos, limit=32): line = [] seen = set() while len(line) < limit: e = self.tt.get(pos.hash) if not e or not e[3]: break mv = e[3] pos.make(mv) if pos.attacked(king of mover, pos.side): # illegal (hash collision) pos.unmake(); break if pos.hash in seen: pos.unmake(); break (or keep? cycle guard) seen.add(pos.hash) line.append(mv) # also stop at no-moves? not necessary # no unmake needed (pos is scratch copy) but fine return line ``` Wait legality check after make: mover = -pos.side. attacked(king_of(-pos.side), pos.side). ✓ info line format: `info depth {d} seldepth {sd} score {cp N | mate N} nodes {n} nps {nps} time {ms} pv {moves}` — time in ms, nps integer (nodes/elapsed). Also `hashfull`? optional skip. Score conversion: ``` if score >= MATE - 1000: m = (MATE - score + 1) // 2 → "score mate {m}" elif score <= -(MATE - 1000): m = (MATE + score + 1) // 2 → wait: score = -(MATE - ply) = -MATE + ply → ply = MATE + score → mated in (ply+1)//2 moves → "score mate -{m}" else: "score cp {score}" ``` Root search function: ``` def _root_search(self, pos, depth, alpha, beta, root_moves, scores): best_move = root_moves[0] best = -INF_BOUND a = alpha # order root_moves: by scores dict desc (missing → -inf), first move = self.best_move if still legal? Simpler: sort by scores.get(mv, -big) — first iteration scores empty → keep MVV-LVA order (pre-sorted). After each iteration scores filled for searched moves. order = sorted(root_moves, key=lambda m: -scores.get(m, -1 << 30)) — hmm scores for unsearched (aborted) moves default low — keeps previous relative order? sorted is stable ✓. legal searched = 0 for mv in order: undo = pos.make(mv) # search if legal searched == 0: sc = -self._negamax(pos, depth - 1, -beta, -a, 1, True) else: sc = -self._negamax(pos, depth - 1, -(a + 1), -a, 1, True) if sc > a and sc < beta: sc = -self._negamax(pos, depth - 1, -beta, -a, 1, True) pos.unmake() scores[mv] = sc (may be bound — ok) if sc > best: best = sc; best_move = mv if sc > a: a = sc # note: best_move only meaningful once sc > a... but keep best as max-sc anyway for ordering if a >= beta: break return best, best_move ``` Note: on abort mid-move, exception propagates; pos corrupted (it's a copy, discarded... wait — _think continues after catching SearchAbort? pos no longer needed except... we use fresh copy for PV ✓. Root `pos` corrupted but we break out of loop ✓ don't reuse.) Also aspiration fail handling must not lose best_move: on fail-low, self.best_move stays previous-iteration's ✓ (we only update after stable in-window result). But careful: if aspiration fail-loops widen and eventually succeed, fine. One more issue: aspiration windows with `beta` possibly less than true score → fail-high → widen beta and re-search. Each re-search costs. With delta 30 and widen 200, converges. On repeated fails, grow: track fail counts: window widening: `alpha -= widen; widen *= 2`? Let me do: on fail low: alpha = score - widen (use returned fail score to reset smarter) with widen doubling: widen = 200; alpha = last - 30 initial; fail low → alpha = score - widen? but score is an upper bound... Actually fail low at root: score = max of null-window results ≤ alpha → true score ≤ score... no wait: root search with window [alpha, beta]: all moves searched with null windows around alpha; returned best ≤ alpha means every move's score ≤ ... PVS: first move searched full [-beta, -alpha]... fail low: best ≤ alpha. The true best score could be anywhere ≤ alpha... it's ≤ best? Fail-soft: best is the max of returned (fail-soft) scores — true score = best (fail-soft negamax returns true max under fail-soft with PVS? With PVS and null windows, non-first moves returning ≤ alpha means their true score ≤ returned value? Null-window search at (alpha, alpha+1) returning v < alpha+1 → true score ≤ v (fail-soft upper bound). So best = max = true root score (upper-bounded). Good: fail low → true score ≈ best (fail-soft). So re-search with alpha' = best - 50? Use: alpha = best - widen (widen=100, doubling). On fail high (score >= beta): true score ≥ beta... returned best is lower bound: set beta = best + widen. These use fail-soft info ✓. Let me define aspiration: ``` if depth >= 4 and last is not None: alpha = last - 35; beta = last + 35 else: alpha = -INF; beta = INF while True: score, mv = root_search(depth, alpha, beta) if score <= alpha: if alpha <= -MATE: break (already full) — set full and retry: alpha = score - 100? widen: w: alpha = max(-INF, alpha - w); w *= 2 → hmm use fail-soft: alpha = score - w... I'll use: w starts 60: on fail low: w *= 4? Let me simplify: alpha = max(-INF, alpha * 2 - score)?? Overcomplex. Plan: if alpha == -INF... just: if alpha > -INF: alpha = score - 2 * abs(score - alpha)? no... Keep simple widening: on fail low: alpha = score - widen; beta stays; widen *= 4. On fail high: beta = score + widen; widen *= 4. Initial widen 60. Since fail-soft score close to true, converges in 1-2 steps typically. Cap: if alpha <= -MATE+... ensure window includes... if score still <= alpha after widen → widen more; loop terminates because window grows each time; when alpha <= -INF and beta >= INF it's full window: fail low in full window impossible (score = true max ≥ ... true max could be ≤ alpha=-INF? no). OK. ``` Termination of aspiration loop guaranteed as window grows unboundedly. ✓ INF bounds: use INF = 1_000_000 (mate 100000). Windows: alpha = max(-INF, ...), beta = min(INF, ...). MDP at root? not needed. Now UCI go parsing: ``` def cmd_go(self, args): if searching: stop and join (robustness) limits: depth=None, nodes=None, mate=None, movetime=None, wtime=None btime=None winc=0 binc=0, infinite=False, ponder=False iterate tokens pairwise: tokens after 'go': for i: key = t[i]; if key in ('depth','nodes','mate','movetime','wtime','btime','winc','binc'): value = int(t[i+1]) with try; 'infinite','ponder' flags. compute: start = perf_counter() soft = None; hard = None if movetime is not None: ms = movetime; soft = start + ms/1000 - 0.002; hard = start + ms/1000 actually soft == hard (one deadline): use hard = start + ms/1000, soft = 0 → after each iteration continue while... for movetime we want to use roughly all: soft = hard? If soft = hard, we stop starting new iterations at hard — effectively uses movetime ✓. Set soft = hard - tiny. elif wtime/btime given (pick side's): t = wtime if pos.side == 1 else btime; inc = winc/binc accordingly if t < 0: t = 0 alloc = t / 30 + inc * 0.5 if alloc < 10: alloc = 10 if t > 0: alloc = min(alloc, t * 0.2)?? hmm if t small, min with t*0.2 tiny... e.g., t=100ms: t/30≈3 → floor 10; t*0.2=20 → alloc 10. hard: min(t*0.5, alloc*6)? t=100: hard=50ms. OK: soft = start + alloc/1000 hard = start + min(t*0.5, max(alloc*5, 30))/1000 ... ensure hard > soft? alloc*5 vs alloc ✓ (≥). min with t/2: if t/2 < alloc*5, hard = t/2 ≥ alloc? t/2 vs alloc (≤ t/5 when capped? alloc cap: min(base, t*0.2) → alloc ≤ 0.2t < 0.5t ✓). ✓ else if infinite or ponder: no deadlines (unless movetime etc. also given? ponder + wtime: deadlines computed at ponderhit — store clock params; on ponderhit compute deadlines then.) else: default: movetime 1000 → as above. depth limit: limits.depth or MAX_DEPTH (64). If mate: max depth 64, stop when found. node limit. store limits in self.limits; start thread. ``` Ponder handling: if ponder: don't start counting; soft/hard = None regardless (even if movetime given); on ponderhit: compute deadlines from stored clock params (recompute using current time) and clear ponder flag; if the search already finished (thread ended?) — ponder search with no deadline runs until max depth 64 (could be minutes in Python? depth 64 never reached — it would run essentially forever until ponderhit/stop ✓ correct behavior). But if search finishes all depths (mate found early break?) — for ponder I should disable the "mate proven break" and single-move break... or if search finishes (returns), thread would print bestmove! During ponder we must NOT print bestmove. Handle: think() at end: if pondering: wait for ponderhit: loop: while self.ponder and not stop: sleep 0.05 (bounded total? infinite). On ponderhit: ponder=False; then... the search already completed; we print bestmove immediately? Yes: ponderhit means "this is the move" → engine replies bestmove (of completed search) ✓. But normal ponder flow: search still running when ponderhit arrives → we want search to now respect time: set deadlines; search's stop checks see hard deadline → abort → print bestmove ✓. In think(), the "wait for ponderhit" loop only happens if search completed early. Combined flow in thread: ``` run ID loop (with ponder → no deadlines, and don't break on mate-proof? keep breaks, fine) after loop: if ponder still set: wait until ponder cleared or stop set (poll 50ms) if stop was set during ponder (GUI aborted ponder): suppress bestmove? Per UCI: on `stop` during ponder → print bestmove! (GUI will send position+go next). Actually UCI spec: "stop" stops search → bestmove printed. Ponder miss flow: GUI sends `stop` (or `ponderhit` if ponder move was right). On stop during ponder → print bestmove ✓. So bestmove printed after wait regardless of ponder/stop path ✓. Only `position`/`ucinewgame`/`quit` suppress bestmove. ``` Wait — on ponderhit, if search was running, deadlines now active... but my ID loop structure: deadlines checked in _check_time (hard) and between iterations (soft). After ponderhit sets hard deadline, search aborts within ~2048 nodes ✓ prints bestmove from last completed iteration ✓. But note the ID loop for ponder ignores "break on soft deadline" (soft None) ✓. Also `go ponder` typically has `wtime btime` — I'll store raw clock params in limits and compute deadlines at ponderhit (fresh) — implement ponderhit handler: ``` def cmd_ponderhit(self): if self.ponder: self.ponder = False recompute deadlines from stored raw time params with current start (soft/hard as in go, but "start" = now) ``` And in _check_time they apply. If no clock params (ponder with movetime?) — handle: if movetime stored: hard = now + movetime. If neither: no deadline (ponder forever until stop — GUI will stop eventually... hmm if ponderhit with no time params, engine should still stop reasonably: use default: set movetime 1000-ish? If no params at all and not infinite — earlier default movetime 1000: store that at go time (limits.default_movetime) and apply at ponderhit. OK.) Simplify limits object: keep raw fields; compute_deadlines(start_time) helper used both at go (non-ponder) and ponderhit. `stop` handler: `self.stop_event.set()` — search thread aborts and prints bestmove (unless suppressed). If not searching: ignore. `quit`: stop_event set, suppress? just set a quit flag; main loop breaks (also join thread briefly with timeout 2s; daemon threads die with process anyway). `ucinewgame`: if searching: suppress=True; stop; join(2); clear tt, history, killers; reset pos to startpos; suppress=False. `position ...`: if searching: suppress=True; stop; join(2); apply; suppress=False. (join timeout: if thread stuck > 2s (shouldn't — abort within 2048 nodes; eval per node... 2048 nodes * ~50µs = 0.1s; fine).) But wait — think()'s finally prints bestmove if not suppress. If we set suppress=True then join(2) — the thread's finally sees suppress True → silent ✓. Race: thread finishing concurrently while we set suppress — either it prints bestmove (GUI might be confused — but that's spec-violating GUI behavior anyway) or not. To reduce race: acquire a lock `self.bestmove_lock` in think's finally around the suppress check & print, and in position handler around suppress set + join. Thread finally: with lock: if not suppress: print. Position handler: with lock: suppress=True; stop.set(); join outside lock (join while holding lock would deadlock since finally needs lock... finally would block on lock → join waits → deadlock. So: with lock: suppress=True. Then stop.set(). Then join. Think finally: with lock: if not suppress: print bestmove. Since suppress set before stop, and think's finally only runs after stop (or natural end — natural end could beat us to the lock: it prints bestmove then; we then set suppress and join a dead thread — outcome: bestmove printed for stale search — GUI sent position mid-search — spec violation; harmless.) ✓ Good enough. Search thread also must not print info lines after being superseded — minor; suppress info too: check `if self.suppress: raise` in _check_time? The abort raise covers: after stop set, abort raised within 2048 nodes; info lines print per completed iteration (before checking stop... order in loop: iteration completes → print info → check stop → break. Slight extra info after stop possible (one line) — GUIs tolerate; or check stop before printing: check `if self.stop_event.is_set(): break` before print. ✓ do that.) Now — "go" while previous search thread still running (spec violation, robustness): stop_event set, join(2), then proceed with new search. stop_event must be cleared before starting new search: `self.stop_event = threading.Event()` fresh per search (clean). And self.nodes etc reset. The engine attributes accessed across threads: keep minimal & atomic. Now, potential runtime error sources to double-check: - IndexError: board access with computed indices — all within 0..119 as analyzed. MIRROR for all sq used (black PST) — only valid squares. Kings: pos.wk/pos.bk could be unset if FEN lacks a king → attacked(-?) ... set wk/bk = 0 if absent; attacked(0,...): board[0-9] = negative index → wraps to board[111]... Python negative index wraps — board[-9] = board[111] exists (120 list) — no crash ✓ but wrong results. Guard: if missing king in FEN — engines shouldn't crash: in set_fen, if king not found: wk = 0; and in search... negamax attacked(king=0) — no crash (just nonsense). Also root move gen: pawn/knight moves fine. "No error in runtime" = no exceptions ✓. Also validate: if side to move has no king → return no bestmove "0000"? Let me guard: in _think: if pos.wk == 0 or pos.bk == 0: pick... simpler: if either king missing → bestmove = first pseudo move or 0000. Minor edge; implement: if not root moves → bestmove (none) → print "bestmove 0000". Generate would still produce moves... Fine, accept nonsense for invalid FEN (no crash ✓). Actually let me add a light FEN sanity: if 'k' and 'K' not both present in piece field → treat as invalid: keep engine position unchanged? position command with bad FEN: per robustness, ignore? Hmm GUIs send valid FENs. I'll parse leniently; if unparseable → fall back to startpos. That prevents weird states. Good: try: set_fen; except: set startpos. - int overflow: none in Python. - Max recursion depth: negamax recursion depth ≈ ply ≤ ~300 < default recursion limit 1000 ✓ (qsearch ply cap 300, negamax ply cap... set MAX_PLY = 200 and cap at 200; qsearch at 200 too. 200 < 1000 ✓ but each frame... Python default limit 1000; frames per negamax call = 1 (plus helper calls not nested) ✓. Actually _check_time → _check_time raises — frames: negamax → qsearch chains: total ≤ 200 nested ✓. Let me set MAX_PLY=200; arrays sized 256. - history/killer array sizes: killers list of 256 pairs, ply < 200 ✓. - Thread daemon=True so quit even if join times out. - `sys.setrecursionlimit(10000)` for extra safety ✓ include. - Windows console: prints flushed ✓ use flush=True everywhere (or print(..., flush=True)). Performance micro-optimizations to apply while writing: - Local aliases inside hot functions: b = pos.board etc. Since Position methods accessed via self — hot functions like negamax call pos.make() (method dispatch). Acceptable. - Zobrist: flat list Z; hash updates in make. - Move ordering: build scored list; sort. Let me also write a quick self-test mode: `python engine.py --test` runs perft assertions (depth 1-4 on startpos + Kiwipete) and a short self-play? Keep: `--perft` option runs perft suite. Nice for validation. Implement perft function outside thread; command `perft N` via UCI too. And internal `--bench`? Skip bench. I'll include a `__main__` with: if `--perft` arg: run perft suite & exit. Else UCI loop. Also add "uci" handshake timing: engine should respond to "uci" promptly ✓. Let me also double check the eval's black PST mirroring for the shield and passed logic once more (done above ✓). Let me also reconsider futility margin values vs eval scale: eval scores typically within ±10 pawns; margins 130/260/430 for depth 1/2/3 seem fine. LMR conditions: `quiet and legal > 3 and depth >= 3 and not in_check` — plus avoid reducing when move gives check? We compute gives_check only in futility branch. For LMR, allow reducing checks (re-search catches). Also avoid reducing killer moves? `mv not in killers[ply]` — cheap check; include (killers shouldn't be reduced). Also avoid reducing when alpha recently improved? skip. Also: LMR when move_count small but depth high and it's a capture? Common to also reduce late captures at big depths; keep quiets-only (safer). Check extension: `if in_check: depth += 1` — unconditional check extension can explode with perpetual checks; standard practice includes it; combined with rep detection returning 0, repetition chains terminate ✓. One more search fine-tune: "TT move ordering at PV: search TT move first even in PV nodes" ✓ via ordering (tt_move scoring top). Now — history indexing: history[side_idx][frm][to]: flat list size 2*120*120 = 28800 ints ✓ init zeros; halve on new search (go): for i in range(len): history[i] >>= 1 — 28800 ops per go ✓ fine. Killers reset each go: killers = [[0,0] for _ in range(256)]. Update: `ks = self.killers[ply]; if ks[0] != mv: ks[1] = ks[0]; ks[0] = mv`. History update: idx = (0 if mover_side==1 else 1)*14400 + frm*120 + to; self.history[idx] += depth*depth; if > 5_000_000... cap: if val > 4_000_000: halve all? Just clamp at 4_000_000 (< killer base 6M? Ensure killer scores above history: killers 6_000_000/5_999_999? history clamp 5_000_000... set killer2 = 5_500_000, killer1 = 6_000_000, history max 5_000_000 ✓. But MVV-LVA base 8M, promo 16M, TT 32M ✓ consistent ordering. Ordering code in negamax: ``` tt_move from probe k1, k2 = self.killers[ply] hist = self.history; base = 14400 if pos.side == -1 else 0 scored = [] for mv in moves: frm = mv & 127; to = (mv >> 7) & 127; promo = (mv >> 14) & 7 cap = b_to_precompute? board[to] — note board currently pre-make ✓ b[to]. if mv == tt_move: s = 32000000 elif promo: s = 16000000 + VAL[promo] elif cap != 0 or ((mv >> 17) & 7) == 1: victim = cap if cap else -side... victim type = abs(cap) or 1 (EP) atype = abs(b[frm]) — piece at frm ✓ s = 8000000 + VT[victim]*10 - VT[atype] where VT = [0,1,2,3,4,5,6] piece type index... define victim/attacker type 1..6; use type*10: s = 8000000 + vtype*10 - atype (small ints) elif mv == k1: 6000000 elif mv == k2: 5500000 else: s = hist[base + frm*120 + to] (cap at 5M via clamp) scored.append((s, mv)) — hmm tuples: sort with key? `scored.sort(reverse=True)` sorts by s then mv ✓ fine. ``` Wait EP flag check: flag==1 → victim pawn: vtype=1 ✓. cap==0 for EP. Combine: `cap_t = cap if cap != 0 else (-1 if flag==1 else 0)`; vtype = abs(cap_t) or 1. Hmm cap for white to move is black piece (negative) — abs ✓. For qsearch ordering: same function minus tt (pass tt_move=0) — reuse `_order(pos, moves, tt_move, ply)`. Now the `_root_search` also uses ordering by previous scores — separate simple sort; fine. Also — important PVS/null-window subtlety at root with alpha == -INF... `-(alpha+1)` overflow? alpha = -1_000_000 → -(alpha+1) = 999999 fine. When alpha = -INF_BOUND, null window (alpha, alpha+1) huge negative window ≈ full window — fine. Hmm, but when beta - alpha == 1 at root (can happen with aspiration? window ≥ 70 wide normally; fail-low re-search could narrow? alpha = score - widen... beta unchanged → window ≥ widen ≥ 60... ok never 1). Aspiration + PVS: at root, if score <= alpha (fail low), best_move from that search is fail-soft max — not necessarily the true best; we do NOT update self.best_move ✓ (only after in-window result). Now — hmm, one thing about `scores` dict used across iterations with fail-soft values: after fail-low re-search, values refresh ✓. Also root: `if a >= beta: break` — with aspiration, beta cutoff at root possible (found score ≥ beta) → that's the fail-high path handled by aspiration loop ✓ (score >= beta → widen). Edge: root_moves ordering first iteration: pre-sort by MVV-LVA — implement by running _order with tt_move=0, ply=0 → sorted captures first ✓ then stable? _order returns sorted scored list — just use it as initial root order ✓. Now `info` string fields — include `hashfull`? skip. Include `ponder` in bestmove line ✓. Next: `d` command output: ASCII board, FEN, and eval maybe. Keep simple: board + fen. Let me also implement `eval` debug cmd: prints "info string eval cp X" or "cp X ..." — just print score. Keep. `go` without position ever set: pos = startpos at init ✓. Let me now also think about the initial `self.pos_lock` usage: go handler copies pos inside think (thread) — main thread could mutate pos via `position` command concurrently... position handler stops the search first (join) before mutating — so no concurrent mutation during think's copy? think() copies at start; if a position command arrives 1µs later: main sets suppress, stop, join — search thread aborts early; fine. But the copy in think happens-before? Race: think starts (copying), main simultaneously applying new position → torn read? Main only mutates after join(2) — the join waits for thread end; but thread just started copying... join waits until think() finishes entirely (including finally + bestmove print? thread finishes). Wait — position handler: suppress=True → stop → join(2): join returns when thread dies. Thread dies after finally (no bestmove since suppressed) ✓. But the search may have already copied and begun searching on old pos — it aborts quickly due to stop flag ✓. And pos mutation by main after join — no concurrent access (thread dead) ✓. Only risk: join timeout (2s) with thread still alive → main mutates pos while thread searches its own copy — thread searches `pos` COPY — main mutates self.pos original — no shared mutable state! Copy semantics: copy() creates new lists ✓. Only shared: TT dict (thread writes, main... ucinewgame clears TT while thread searching → dict cleared during search: thread handles fine (misses). Rep list — copied ✓. So even timeout is safe. One more shared: self.limits — set before thread starts, not mutated during. ✓ self.killers/history — only thread touches (main resets at go before thread start ✓ after join). Actually ucinewgame after timeout-join failure could reset killers while thread uses — killers list replaced (new object) — thread uses self.killers? If thread reads self.killers each access vs captured local... I'll have search capture references at start; main replacing self.killers with a new list wouldn't affect the thread's captured local ✓. Design: in think(): killers = [[0,0] for _ in range(256)]; self.killers = killers (for potential debugging) — mutations on the list by thread only. Fine. Exception inside thread → caught → traceback to stderr → bestmove fallback printed ✓ (protocol continues). Let me now also handle `isready` → always reply readyok ✓ (even during search — main thread). `uci` → print id lines + uciok. Also print "option" lines? none. OK — also the FEN parse details: ``` def set_fen(self, fen): parts = fen.split() if len(parts) < 1: raise ValueError board part: rows = parts[0].split('/'); expect 8 rows; for r_idx, row in enumerate(rows): rank = 7 - r_idx; file = 0; for ch: digit → file += int; else piece = PIECE_FROM_CHAR[ch] (map 'P'→1, 'N'→2, 'B'→3, 'R'→4, 'Q'→5, 'K'→6, lowercase negative); board[21 + rank*10 + file] = piece; file += 1 validate: each row sums to 8? If malformed, raise → caller falls back to startpos. I'll validate sum == 8 and 8 rows. side = parts[1] 'w'/'b' (default w if missing) castling = parts[2] if len>2 else '-': map K→1 Q→2 k→4 q→8; ignore others; '-'→0. Also validate: only add right if king/rook on home squares? Lenient: accept as given (GUIs send correct). Hmm — but if castling rights say K but rook not on h1, our castle gen could create nonsense... gen checks squares empty + not attacked, and make moves rook from 28 — if no rook there, move makes king move with rook appearing! Guard at gen: also require b[28] == 4 for WK right etc. — cheap robustness: in castle gen condition include board checks: white K-side: rights&1 and b[25]==6 and b[28]==4 and b[26]==0 and b[27]==0 ... ✓ include (also protects corrupted states). ep = parts[3]: '-' → 0; else parse square like 'e3' → sq. Validate rank in 3..6? Accept any valid square; store. halfmove = int(parts[4]) if len>4 and valid else 0; fullmove = int(parts[5]) if valid else 1. kings: scan board for 6 and -6. hash = compute; rep = [hash]; undo stacks cleared. ``` to_fen: reverse ✓. UCI square name: chr(ord('a') + file) + str(rank + 1). parse square: 'a'..'h' → file, '1'..'8' → rank → sq = 21 + rank*10... wait rank index = int(ch)-1 → sq = 21 + (int-1)*10 + file. Also fullmove tracking: make increments fullmove when black moves; to_fen uses it. Minor; implement. Now `position` handler: ``` tokens after 'position': if tokens[0] == 'startpos': fen = START_FEN; rest after 'moves' elif tokens[0] == 'fen': collect tokens until 'moves' → fen string = join; rest. apply set_fen(fen) (fallback startpos on error) for each uci move in rest: mv = parse against generated legal moves; if found: make (legality already ensured by matching against legal list: build legal list per step: gen + make-test + unmake, collect, match string) else break. ``` Match: convert each legal move to uci string and compare — simplest ✓ (cost trivial). Alright — also `bestmove` when root has no moves: `bestmove 0000`? Hmm, spec expects a move; GUIs shouldn't ask. Also print "(none)"? Standard engines print `bestmove 0000`. I'll do that. Now let me also design the SearchAbort check to include "soft deadline only checked between iterations" ✓. Wait — one more thing: hard deadline check must also account "go infinite" (no deadline) ✓ None. `go movetime` soft/hard: soft = hard = deadline; between iterations check `elapsed >= soft` → stop starting new iterations ✓; within search, hard abort ✓. For movetime, after first iteration completes at 1s elapsed, we stop — engine uses ~movetime ✓. Also add small safety: always search at least depth 1 fully even if movetime tiny? If hard deadline already passed at node 2048 during depth-1 search → abort → best_move fallback (root[0], MVV-LVA ordered) ✓ no crash ✓. Let me also make sure `_check_time` not called before limits set (perft path doesn't search ✓). `nps` computation: nodes / elapsed seconds (guard elapsed 0 → use max(elapsed, 1e-9)). info time: int(elapsed*1000). Now — think about the `_check_time` TT-size clear: len(self.tt) > TT_MAX → self.tt.clear() — but local alias `tt` in negamax? I access self.tt each probe (attribute) — I'll use self.tt directly in negamax (attribute lookup each probe — fine). Actually for speed, alias tt = self.tt at think() start and pass around? Functions are methods; aliasing inside each call: `tt = self.tt` once per negamax call — ok. Performance estimate: negamax per node: several method calls, sort with tuples, evaluate at leaves. Expect ~3-8k nps. Depth 5-7 in a second-ish. Modest but works. It's Python — acceptable. Mention in notes. One more feature for strength: "capture the flag"... no. OK. Edge: `attacked()` for king of side... in negamax: `king = pos.wk if pos.side == 1 else pos.bk; in_check = pos.attacked(king, -pos.side)` ✓. In qsearch in-check branch: moves generated = all (not caps_only) ✓. Now — one more important correctness item: PVS re-search condition `if score > alpha and score < beta` — with null-window (alpha, alpha+1) the returned score when > alpha means it's ≥ alpha+1 → could be ≥ beta? Null window beta = alpha+1; fail high means score ≥ alpha+1; could be ≥ real beta → then re-search with full window needed... condition `score > alpha` (regardless of < beta) → re-search full window if also score < beta; if score >= beta, full-window re-search unnecessary (it's ≥ beta; but fail-soft value unreliable for cutoff? PVS standard: if score > alpha and score < beta: re-search; else use score (if ≥ beta it's a beta cutoff ✓ fine). So my code: ``` if legal == 1: full window else: sc = -nullwin(depth-1-r) if sc > alpha and r > 0: sc = -nullwin(depth-1) (full-depth null window) if sc > alpha and sc < beta: sc = -fullwindow(depth-1) ``` Wait if r>0 and sc > alpha: full-depth null-window re-search; then if that > alpha and < beta: full window. If sc > alpha but sc >= beta → cutoff with sc ✓ (no full window needed — but sc from reduced/null window ≥ beta is a valid lower bound ✓ fine). Hmm — but subtle: after LMR re-search at full depth null-window returns sc > alpha but ≥ beta? Then cutoff ✓. OK. And for r == 0 path (non-first, non-reduced): sc = nullwin; if sc > alpha and sc < beta: full window ✓. else if sc > alpha (≥ beta) cutoff ✓. Code: ``` if legal == 1: score = -negamax(depth-1, -beta, -alpha) else: red = 0 if quiet and legal > 3 and depth >= 3 and not in_check and mv != k1 and mv != k2: red = 1 if legal > 12: red = 2 if depth >= 7 and legal > 20: red = 3 score = -negamax(depth-1-red, -(alpha+1), -alpha, ply+1) if score > alpha and red: score = -negamax(depth-1, -(alpha+1), -alpha, ply+1) if alpha < score < beta: score = -negamax(depth-1, -beta, -alpha, ply+1) ``` ✓. Root similar. Now write everything out. Also `if __name__ == "__main__":` — parse argv: `--perft` runs suite; `--fen "..." --depth N` maybe quick eval? skip. Also handle BrokenPipeError on prints (GUI closed): wrap main loop print failures → exit gracefully. `print(..., flush=True)` may raise BrokenPipeError → catch in a helper `out()` function that try/excepts and sets quit flag. Good for "no error in runtime". Let me write the final code now, carefully, and mentally simulate a few sequences: Simulate: startpos, `go depth 4` — root gen 20 moves; search; TT stores; PV prints; bestmove e2e4-ish (depends). Fine. Castling squares recheck: white king e1 = 21+0*10+4 = 25 ✓ (a1=21, b1=22, c1=23, d1=24, e1=25, f1=26, g1=27, h1=28 ✓). Black: e8 = 21+70+4 = 95 ✓; a8=91, h8=98 ✓. White pawn double: from 31..38 → to 51..58; ep square set = to-10 = 41..48 (rank 3 index 2) ✓ (e3=45: 21+20+4=45 ✓). EP capture: black pawn on 44 (d4)? White pawn e4 = 21+30+4=55? e4: file e=4, rank 3 → 21+30+4 = 55 ✓. Black pawn d4 = 21+30+3 = 54. White pawn 55 captures ep at 54-10+... ep square after black d7d5: d5 = 21+40+3 = 64; ep square = d6 = 63? Wait: black double push d7d5: from 81+3=84 → to 64 (d5: rank 4 index? d5 = file 3, rank index 4 → 21+40+3 = 64 ✓). New ep square = to + 10 = 74 (d6 ✓). White pawn on e5 (21+40+4=65) captures to 74 (d6): flag EP; victim pawn at 74-10=64 (d5) ✓ correct. Black EP: white double e2e4 → ep = to-10 = 55 (e3 ✓). Black pawn d4 (54) captures to 55: victim at 55+10 = 65 (e4 ✓). Castle white kingside: king 25→27; rook 28→26 ✓. Queenside: 25→23; rook 21→24 ✓ (d1=24 ✓). Promotion: white pawn at 91..98 pushes to 101..108?? Wait: white pawn on rank 7 index 7? White pawn promotion happens pushing from rank 7 (index 7? no — rank index 7 = 8th rank... pawns promote moving to the 8th rank: from rank index 6 (7th rank, squares 81..88) to rank index 7 (91..98). So white pawn at sq 81..88 → to 91..98, to >= 91 ✓ promo check ✓. My gen: `to = sq + 10; if b[to] == 0: if to >= 91: promos` ✓. And captures: to = sq+9/11 ≥ 90... to ≥ 91 check ✓ (sq 81: to = 90/92: 90 is not ≥91... sq 81 (a7) capture b8: 81+11 = 92 ✓ ≥91 ✓; 81+9 = 90 = off-board? a7 is file 0; +9 = file 1 rank 7 = b8 = 21+70+1 = 92. Hmm 81 + 9 = 90; b8 = 92?? Let me recompute: a7 = 21 + 60 + 0 = 81 ✓ (rank index 6). b8 = 21 + 70 + 1 = 92. 81 + 11 = 92 ✓ (NE direction = +11 ✓ file+1, rank+1 ✓). +9 = NW (file-1): from a7 file 0 → off-board ✓ b[90] = OFF ≠ enemy ✓ skipped ✓. Great. Black pawn promo: black pawn at 31..38 (rank 2 index 1) moving to 21..28 ✓ `to <= 28` check ✓ (to = sq-10 ∈ 21..28 ✓). Also `to >= 21` always true (sq min 31 → to 21 ✓). White pawn gen: `if sq <= 38` for double — sq ranges 31..38 ✓ (white pawns never below 31 in legal chess; but corrupt FEN could have pawn on rank 1 — sq 21..28: sq <= 38 true → double push from rank1?? b[sq+20] rank 3 — nonsense move but harmless (pseudo-legal in corrupt position; legality filter passes; fine — no crash). Knight offsets verify: +8: file+... +8 = -2 rank? 8 = 10-2: rank+1, file-2 ✓ a knight move ✓. -8: rank-1, file+2 ✓. +12: rank+1, file+2 ✓. -12 ✓. +19: rank+2, file-1 ✓. -19 ✓. +21: rank+2, file+1 ✓. -21 ✓. Now evaluate PST for black pawn at e7 (21+60+4=85): mirror = 21+0*10+4 = 25 (e1... hmm e2? 25 = e1) → table lookup index (7 - r)... Let me verify the mirror maps to PST correctly: white pawn at e2 (rank idx 1) gets table row (7-1)=6: values [5,10,10,-20,-20,10,10,5] file e → -20? e2 pawn: row6 col4 = -20 ✓ (encourages e4 push? e4 = row 4 col 4 = 0? row 4: [0,0,0,20,20,0,0,0] col 4 = 20 ✓ +20 for e4/d4 ✓ good). Black pawn e7 (rank idx 6, file 4): mirror sq = 21 + (7-6)*10 + 4 = 35 (e2 ✓) → PST_MG[1][35] = same as white pawn at e2 = -20 ✓ black subtracts: mg -= (100 + (-20)) → -80 → black pawn e7 slightly penalized ✓ symmetric ✓. Now, let me also verify VAL & NPM: VAL = [0,100,320,330,500,900,0] (index by piece type abs; king 0 — used only in MVV-LVA victim (never) and delta (never)). King "value" in MVV-LVA: VT type index 6*10 = 60 — could make king captures rank high — kings never captured in legal play; in MVV-LVA attacker king fine. OK. MATE = 100000; INF_BOUND = 1000000. Draw score 0 ✓. Insufficient material: phase <= 2 and no pawns → return 0. But careful: phase counts both sides' pieces: K+B vs K+N = phase 2 ✓ 0. K+R vs K = phase 2!! Rook phase = 2 → wrongly 0!! Rook is phase 2, so K+R vs K (phase 2) would return draw — WRONG (KR vs K is a win). Fix: insufficient condition must exclude rooks/queens: check via piece counts: no pawns and no rooks and no queens and minors_total <= 2 → 0. Track rook counts: len(wr)+len(br) etc. Let me restructure: track counts: wnp_, bnp_ etc. Simplest at end of scan: `if not any_pawns and (wr or br or wq or bq)... ` — track wq/bq counts too. Condition: no pawns AND no rooks AND no queens AND (wn+wbi + bn+bbi) <= 2 → return 0. KR vs K → rook exists → not 0 ✓. KB vs KB → minors 2 → 0 ✓. KBB vs K → minors 2 → 0?? KBB vs K is a win (two bishops)! Bishops on same color... KBB usually wins. minors <= 2 includes KBB (2 white bishops) → returns 0 — slightly wrong eval for KBB vs K. Accept? It's a rare fortress-ish case; typical engines use more nuance. Meh — refine: return 0 if minors <= 1 per side and total minors <= 2... KBB: white minors 2 > 1 → not forced 0, eval proceeds with material 660 + bishop pair → wins ✓. KBN: white 2 minors → eval proceeds (material 650 — KBN is a win ✓ good). KB vs KB: each side 1 → 0 ✓. KN vs KN ✓ 0. KB vs KN ✓ 0. So condition: no pawns/rooks/queens and wmin <= 1 and bmin <= 1 → 0 ✓. And also K vs K ✓. Now — the search's use of `pos.evaluate()` — method on Position ✓. Let me also handle `pos.in_check()` helper: attacked(own king, -side) — used in gen castle (attacked calls explicit). Now writing `think` info output: ``` pv = self._pv_line(root_copy) where root_copy = engine.pos copy? Use fresh copy of self.pos (engine's true position) — hash must match search root ✓ same position → TT hits ✓. ``` Print: `info depth {d} seldepth {sd} score {s} nodes {n} nps {nps} time {ms} pv {line}`. Ponder move for bestmove: pv[1] if len(pv) > 1 and pv[0] == self.best_move. Also print info even at depth 1 ✓. Now — "go mate N": after iteration: if score >= MATE - 1000 and mate_moves = (MATE - score + 1)//2 <= N → break ✓. (Even if score is negative mate (we're being mated) — keep searching? If mated, engine can't do better; break too? If score <= -(MATE-1000): we're getting mated — continue searching for best defense... if the mate found for opponent is within N? The command means "find mate in N" — only break on winning mate ✓.) Also "mate found break" for normal searches: if score >= MATE - 1000 and depth >= (MATE - score) + 2 → break? Hmm — mate scores stable; break when depth > mate distance to save time: `if score > MATE - 1000 and depth >= MATE - score: break` — include for time savings. Careful with aspiration: mate score found at depth d — deeper iterations might find shorter mate, but breaking is fine (we have A mate). Include. Actually — careful edge: score = MATE - ply where ply ≤ current depth... if aspiration fails... only break after in-window iteration ✓ placement after stable result. Hmm wait, one thing: should I break when winning mate found? If we stop at depth 6 having found mate in 3, fine. But at depth 2 with mate score from a shallow tactic that's real... mate scores are exact proofs (search proves forced mate within depth horizon... with TT cutoffs and qsearch — mate found via search is genuine (search proved no escape within horizon... a "mate" score from TT with mate adjustment could be from a different path — standard risk, accepted). OK. Also handle `stop` before any iteration completes → bestmove fallback root[0] ✓. Now the perft suite for --perft: ``` positions: startpos: [20, 400, 8902, 197281] depth 4; kiwipete r3k2r/p1ppqpb1/bn2pnp1/3PN3/1p2P3/2N2Q1p/PPPBBPPP/R3K2R w KQkq - : [48, 2039, 97862]; position 3: 8/2p5/3p4/KP5r/1R3p1k/8/4P1P1/8 w - - : [14, 191, 2812, 43238]; position 4: r3k2r/Pppp1ppp/1b3nbN/nP6/BBP1P3/q4N2/Pp1P2PP/R2Q1RK1 w kq - 0 1: [6, 264, 9467]; position 5: rnbq1k1r/pp1Pbppp/2p5/8/2B5/8/PPP1NnPP/RNBQK2R w KQ - 1 8: [44, 1486, 62379]; position 6: r4rk1/1pp1qppp/p1np1n2/2b1p1B1/2B1P1b1/P1NP1N2/1PP1QPPP/R4RK1 w - 0 10: [46, 2079, 89890]. ``` These are the standard CPW perft values. I'm fairly confident about: startpos 20/400/8902/197281 ✓; Kiwipete 48/2039/97862 ✓; pos3 14/191/2812/43238 ✓; pos4 6/264/9467 ✓ (pos4 depth 3 = 9467 ✓); pos5 44/1486/62379 ✓; pos6 46/2079/89890 ✓. These are well-known. Include depths up to 3 (Python speed) — startpos depth 4 optional (197k nodes ~ maybe 2-6s). I'll run to the listed depth but make suite tolerant: run each to depth 3 default; `--perft 4` for more. Keep simple: fixed small suite depth 3, print results, compare expected, exit code. Now, is there anything about "Minimax" the user explicitly listed — they listed both minimax and alpha-beta. My negamax is minimax via symmetry; I'll note in comments: "negamax formulation of minimax". Good. Also they said "Much fine-tuned search policy" — I have: iterative deepening, aspiration windows, PVS, TT, null-move, LMR, RFP, futility, check extension, quiescence with delta pruning, killers, history, MVV-LVA, mate-distance pruning, TT-move ordering, repetition/fifty handling, time management with soft/hard limits, ponder. That's a solid list. I'll document these in the module docstring. Now let me write the code. I'll aim for clean, commented, ~900 lines. Let me write it in final form, being careful with every detail. I'll go section by section. ```python #!/usr/bin/env python3 """ PyChess — a self-contained UCI chess engine in pure Python. Features: * Complete rules: castling (incl. through-check rules), en passant, promotions, check/checkmate/stalemate, 50-move rule, repetition. * Search: iterative deepening, negamax (minimax) with alpha-beta, principal variation search, transposition table (Zobrist hashing), null-move pruning, late move reductions, reverse & forward futility pruning, mate-distance pruning, check extensions, quiescence search with MVV-LVA + delta pruning, killer moves, history heuristic, aspiration windows, time management (soft/hard limits) and pondering. * Evaluation: material + piece-square tables (tapered mid/endgame), pawn structure (doubled/isolated/passed), rook files, bishop pair, king pawn-shield, tempo, insufficient-material draw detection. * UCI: uci, isready, ucinewgame, position, go (depth/nodes/movetime/ wtime/btime/winc/binc/infinite/mate/ponder), stop, ponderhit, quit, plus debug commands: d (board), eval, perft . Run: python pychess.py (UCI mode, talk to it via stdin/GUI) python pychess.py --perft (self-test: standard perft suite) No external dependencies. """ import sys import time import random import threading import traceback ``` Constants: ```python # ------------------------------------------------------------------ board OFF = 100 # off-board sentinel EMPTY = 0 WP, WN, WB, WR, WQ, WK = 1, 2, 3, 4, 5, 6 BP, BN, BB, BR, BQ, BK = -1, -2, -3, -4, -5, -6 VAL = (0, 100, 320, 330, 500, 900, 0) # material by |piece|, king=0 NPM = (0, 0, 320, 330, 500, 900, 0) # non-pawn material by |piece| PHASE = (0, 0, 1, 1, 2, 4, 0) # game-phase weight KNIGHT_OFF = (8, -8, 12, -12, 19, -19, 21, -21) KING_OFF = (10, -10, 11, 9, -9, -11, 1, -1) BISHOP_DIR = (11, 9, -9, -11) ROOK_DIR = (10, -10, 1, -1) ALL_SQ = [21 + 10 * r + f for r in range(8) for f in range(8)] def rank_of(sq): return sq // 10 - 2 def file_of(sq): return sq % 10 - 1 MIRROR = [0] * 120 for _sq in ALL_SQ: MIRROR[_sq] = 21 + 10 * (7 - (_sq // 10 - 2)) + (_sq % 10 - 1) # castling-right masks: moving from/to these squares strips rights CMASK = [15] * 120 CMASK[25] = 15 & ~3 # e1 -> lose both white rights CMASK[21] = 15 & ~2 # a1 CMASK[28] = 15 & ~1 # h1 CMASK[95] 15 & ~12 CMASK[91] = 15 & ~8 CMASK[98] = 15 & ~4 ``` Wait — bits: 1=WK (kingside white), 2=WQ, 4=BK, 8=BQ. h1 rook → lose WK (bit 1) ✓ CMASK[28] = ~1 & 15 = 14 ✓. a1 → lose WQ bit 2 → 13 ✓. e1 → lose 1|2 → 12 ✓. e8 → lose 4|8 → 3 ✓. a8 (91) → lose BQ (8) → 7 ✓. h8 (98) → lose BK (4) → 11 ✓. PSTs (write tables, build): ```python PST_MG = [None] * 7 PST_EG = [None] * 7 ``` Table data as listed above (row 0 = rank 8, from white's view). Build arrays for 120 with white perspective; black uses MIRROR. Zobrist: ```python _rng = random.Random(0x9E3779B97F4A7C15) # fixed seed → reproducible ZOBRIST_PIECE = [_rng.getrandbits(64) for _ in range(13 * 120)] ZOBRIST_SIDE = _rng.getrandbits(64) ZOBRIST_CASTLE = [_rng.getrandbits(64) for _ in range(16)] ZOBRIST_EP = [_rng.getrandbits(64) for _ in range(8)] ``` Index: (piece + 6) * 120 + sq. piece+6 for -6..6 → 0..12; index 6 (empty) unused garbage — fine. Move encode/decode helpers as module functions. Position class: ```python class Position: __slots__ = ("board", "side", "castling", "ep", "halfmove", "fullmove", "wk", "bk", "hash", "rep", "undo", "nundo", "wnpm", "bnpm") ``` __slots__ for speed ✓. set_fen, to_fen, copy, compute_hash, attacked, generate, make, unmake, make_null, unmake_null, evaluate, is_rep, in_check, move helpers. Let me write generate fully (both colors, caps_only param). Also note the promo codes in move: promo ∈ {2,3,4,5}. flag ∈ {0,1,2,3}. Move int: frm | to<<7 | promo<<14 | flag<<17. UCI move string: sq_name(frm) + sq_name(to) + ("nbrq"[promo-2] if promo). "nbrq"[promo - 2]: promo=2→'n', 3→'b', 4→'r', 5→'q' ✓. Now the Engine class. Let me also keep the search state on self for simplicity: nodes, seldepth, stop_event, hard_deadline, soft_deadline, node_limit, tt, killers, history, best_move, ponder... Search functions as methods: _negamax, _qsearch, _root_search, _think, _pv_line, _check_time. Time to write the full code. I'll write it out now in near-final form, then re-verify tricky bits. Also: `evaluate` must be fast — write with local vars. One more check — attacked() usage in castle gen: `self.attacked(25, -1)` for white castling (attacked by black) ✓. Position.evaluate() returns from side-to-move perspective ✓ (used in negamax/qsearch directly). Insufficient material check inside evaluate (after scan): ```python if (not any pawn lists) and (wr or br) is False... ``` track wq_cnt/bq_cnt? I count pieces: during scan track counts via variables wn_c, wb_c, wr_c, wq_c, and black. Then: ```python no_pawns = not (any(wp) or any(bp)) # any() on list of lists: non-empty sublist → True ✓ if no_pawns and wr_c == 0 and br_c == 0 and wq_c == 0 and bq_c == 0 \ and (wn_c + wb_c) <= 1 and (bn_c + bb_c) <= 1: return 0 ``` any(wp) where wp is list of lists: True if any file has pawns ✓. Now — full code writing. Let me be extra careful in `make` with hash for castling when rights unchanged (skip XOR) ✓, ep file extraction: `old_ep % 10 - 1` valid since ep square on board ✓. Also in make, `new_ep` computed from flag==2 — note the check uses b[to±1] AFTER the pawn moved to `to` — b[to] set; adjacent squares are other pieces ✓ correct. Also careful: the "adjacent enemy pawn" check for setting ep: white pushes to `to` (rank 3 index 2... wait white double push: from rank idx 1 to rank idx 3 (to = 51..58); ep square = to - 10 = 41..48 (rank idx 2 ✓ 6th... no rank idx 2 = 3rd rank ✓ ep square is on 3rd rank for white double push ✓ correct — ep target e3 = 45? White pushes e2e4: ep = e3 = 45 = to - 10 where to = e4 = 55 ✓ ✓. Black: to = 71..78 (rank idx 4, 5th rank); ep = to + 10 = 81..88?? That's 7th rank — WRONG. Black double push d7d5: ep square should be d6 (rank idx 5, 71-78). to = d5 = 64; ep = 64 + 10 = 74 = d6 ✓. Wait I said to = 71..78 — no: black pawn from rank idx 6 (81..88) to rank idx 4 (61..68)! to = sq - 20: 81-20 = 61..68 ✓. ep = to + 10 = 71..78 (rank idx 5 = 6th rank ✓). Good — earlier I confused myself; code: `new_ep = to + 10` for black ✓. White: to = sq + 20 = 51..58 (rank idx 3 ✓ 4th rank); ep = to - 10 = 41..48 (3rd rank ✓). Adjacent pawn check for black: `b[to - 1] == 1 or b[to + 1] == 1` (white pawns beside, on 4th rank... white pawns that can ep-capture black's double push sit on rank idx 4 (5th rank)?? Black pushes d7d5 → white pawn on c5/e5 (rank idx 4) can capture d6 ep. White pawn on rank idx 4 = squares 61..68. But my check reads b[to±1] where to = 61..68 ✓ — pawns adjacent on the 5th rank ✓ correct! Similarly white double: black pawns adjacent on rank idx 3 (4th rank squares 51..58) = b[to±1] with to = 51..58 ✓. Great. Now — negamax TT probe at PV vs non-PV: `if ed >= depth and ply > 0 and (beta - alpha) > 1:` — hmm this excludes PV nodes entirely; aspiration root children have beta-alpha maybe 70 → PV nodes → no cutoffs there ✓ fine (root children = PV). Actually wait — beta-alpha > 1 identifies non-PV; aspiration windows (35±) produce beta-alpha = 71 → considered non-PV at root children → TT cutoffs there are on the PV path! Cutoffs on PV nodes with narrow aspiration windows: a TT exact score from same position with sufficient depth is fine to return even on PV... Risk: PV from TT exact is fine. LOWER/UPPER cutoffs on PV nodes could cause "PV truncation" but scores remain correct bounds — the returned score might be a bound rather than exact — aspiration handles. Common engines do allow TT cutoffs everywhere except root. Let me allow cutoffs at all plies>0 regardless of PV: `if ed >= depth and ply > 0:`. This is what e.g. many engines do (with the caveat of search instability). But there's a known subtlety: in PV nodes, cutting with a LOWER bound (fail-high stored) is fine because it's ≥ beta... LOWER means score >= beta at store time — cutoff requires es >= beta now ✓ consistent. UPPER: es <= alpha ✓. EXACT ✓. It's sound given TT correctness. The only issue: PV gets shortened/instability. Acceptable — but then aspiration + TT: on fail-low, the aspiration loop re-searches; TT with old bounds... e.g. stored UPPER from previous depth with ed >= depth — cutoff at PV child returns ≤ alpha → root fails low → widen ✓ converges. OK, allow cutoffs at ply > 0 (not root). This strengthens move ordering/TThit benefits. Hmm, but a subtle trap: with TT cutoffs in PV, the root best move may come from a TT cutoff at the root child... root children are searched via negamax(ply=1) — ply>0 → cutoffs allowed → root's "score" for a move could be a TT bound... root then may pick a move whose exact score unknown but bounded — with aspiration windows this is standard behavior in real engines. Fine. BUT careful: TT cutoff returning at ply==1 with LOWER bound from a previous search where that move wasn't best... it's bounds-based; sound per alpha-beta semantics. OK. One more standard safety: don't return TT cutoff when halfmove near 100 or rep-prone (TT ignores path) — could return a "winning" score in a drawn-by-repetition path. Standard engines accept this (draw detection usually still triggers since rep checked before TT probe — my order: rep/fifty checked BEFORE TT probe ✓ good; that mitigates). Now, `_check_time` also handles the TT clear and node limits. And `self.nodes` shared attribute — increments via `self.nodes += 1` (attribute access; could use a local list... keep simple). Alright — also `seldepth`: `if ply > self.seldepth: self.seldepth = ply` in both searchers. Let me now also double-check `_root_search` abort: negamax raises SearchAbort; _root_search doesn't catch → propagates to _think's try ✓. But — pos left corrupted (undo stack). _think catches; best_move retained from earlier ✓. pos object discarded ✓ (thread-local copy). BUT — think holds `pos` copy; on next `go`, fresh copy from engine.pos ✓ clean. Wait, actually one more: _think catches SearchAbort around the whole ID loop; after catch, still prints info? No — just proceed to final bestmove print. ✓ Also: on catch, update info line with " Partial search aborted"? skip. Now `go ponder` + ponderhit while thread waiting in ponder-wait loop: ponderhit sets ponder False and sets deadlines — the wait loop exits (polls self.ponder) — then thread prints bestmove of completed search. But ideally after ponderhit with time limits, we'd continue searching until deadline — we finished all depths anyway (wait loop only entered after search completed). Fine. The ponder-wait loop must also exit on stop (prints bestmove ✓ per spec) and on suppress (position/quit → don't print). Implement: ```python if self.pondering: while True: if not self.pondering or self.stop_event.is_set() or self.suppress: break time.sleep(0.02) ``` then finally: if not suppress: print bestmove (even if stop) ✓. If suppress (position arrived): no print ✓. ponderhit handler: set deadlines: `self.pondering = False; self._apply_clock(self.limits, start=now)`. Where _apply_clock computes soft/hard from stored limit fields (movetime/wtime/...). At `go`: if ponder: pondering=True, no deadlines; else apply clock with start=now. For `go infinite`: pondering False, no deadlines, max depth 64, loop until stop. Note: `go ponder infinite`? weird combo — ponder wins: pondering=True, no deadlines ✓ fine. `go ponder wtime X ...`: store; at ponderhit apply ✓. Edge: `stop` while ponder-wait loop → break → print bestmove ✓ (spec-correct: stop during ponder prints bestmove? UCI spec: "stop... if pondering, stop calculating but don't print bestmove?" Hmm. Actually the UCI spec says: If the engine is pondering and receives "stop", it should... Let me recall precisely: UCI says ponderhit means "the opponent played the move the engine was pondering on" → engine switches to normal search. "stop" during ponder → GUI aborted (ponder missed or GUI closing) → engine should stop and... the spec for stop: "stop calculating as soon as possible" — and bestmove is printed for the current search. For pondering, most engines print bestmove on stop (arena expects?). Actually — on ponder miss the GUI sends `stop` then `position ... go ...`; engines print bestmove after stop (GUI ignores it). Printing is the safe standard behavior (spec: "the engine must answer with bestmove"). Hmm, but the ponder-miss case: printing bestmove is what Stockfish does (it always prints bestmove on stop). And on `position` while searching, SF doesn't get into that state... GUIs send stop first. Our behavior: stop → bestmove; position → suppress+stop → silence. Matches SF closely enough ✓. Now — one more potential runtime error: `position startpos moves e2e4 e7e5 ...` with move parse using uci string of length 4/5. `sq parse`: guard len >= 4, chars valid. If invalid → break silently. Also — "position fen" with fewer than 6 fields (GUIs sometimes send 4): set_fen handles missing fields with defaults ✓ (parts length checks). Halfmove parse: could be "-"? Rare; try/except int → 0 ✓. Now — code for `d`: ```python def cmd_d(self): print(self.pos.board_str() ... ) ``` board_str: 8 rows from rank 8 down: for r in 7..0: line with piece chars '.' empty. piece char map: {1:'P',2:'N',3:'B',4:'R',5:'Q',6:'K',-1:'p',...}. Also print "Fen: " + to_fen(), "Eval: " maybe. Keep board + fen. Check `to_fen` correctness: castling string: build from bits in order KQkq ✓ '-' if none. ep: sq_name or '-'. halfmove, fullmove ✓. Let me also handle fullmove increments: make: `if side == -1 (black just... wait side before flip: if self.side == -1 (black moving): fullmove += 1` — in make before flipping side, side is mover: if mover black → fullmove += 1 ✓. unmake: if mover black (after restore side) → fullmove -= 1 ✓. Now — engine `__init__`: pos = Position(); pos.set_fen(START); tt = {}; history zeros; stop_event fresh; threads none; pondering False; suppress False. `START_FEN = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"`. Main UCI loop: ```python def uci_loop(engine): out id on "uci"... while True: line = sys.stdin.readline() if not line: break tokens = line.split() if not tokens: continue cmd = tokens[0].lower() dispatch... if cmd == "quit": engine.quit(); break ``` readline blocks; on EOF returns '' → break ✓. Note `for line in sys.stdin` buffering is fine too; readline is more responsive. Use sys.stdin.readline(). Exceptions in main loop: wrap dispatch in try/except → print info string error to stderr, continue ✓ (never crash). Let me also add `out()` helper printing with flush and BrokenPipe guard: ```python def out(text): try: print(text, flush=True) except (BrokenPipeError, OSError): sys.exit(0) # hmm inside thread sys.exit raises SystemExit in that thread only — fine, kills thread. In main thread exits. ``` Actually in search thread, sys.exit() raises SystemExit → caught by my generic except in think → prints to stderr... whatever. Use os._exit? Too harsh. Just swallow and set engine.dead flag → thread returns. Keep simple: try print except: pass, and set a global _broken flag; loop checks? Overkill — swallow. Also stderr errors: print to sys.stderr (unbuffered-ish) fine. Now write the search with all the parameters. Let me finalize numbers: - ASPIRATION: from depth 4: window ±35; widen: on fail low: alpha = score - widen (widen starts 120, *4 each fail... let me: widen = 120; fail low → alpha = score - widen; widen *= 4. fail high → beta = score + widen; widen *= 4.) Also if alpha < -INF... clamp. - NMP: depth >= 3, npm >= 300 for side... npm values: N=320 ✓ B=330 ✓; require `>= 300` means knight/bishop qualifies ✓; pawn-only endings excluded ✓. R = 2 + (depth >= 6) + (depth >= 12)? Use R = 2 if depth < 6 else 3. - RFP: depth <= 5, non-PV, not in check, margin = 90 * depth: `if se - 90 * depth >= beta: return se` — careful with se sign perspective (side to move) ✓ and beta bounds not mate: `if beta < MATE - 1000 and beta > -(MATE - 1000)`. - Futility (forward): depth <= 3, quiet, move_count >= 2 (legal > 1), not in check, no check given, `se + FUT[depth] <= alpha`, FUT = (0, 130, 260, 430). Guard alpha not near mate: `abs(alpha) < MATE - 1000`? If alpha is a mate score, futility skip — fine to include guard. Hmm also don't run futility when beta-alpha... any node ✓ fine. - LMR: depth >= 3, legal > 3, quiet, not in_check, not killer: red = 1; legal > 12 → 2; depth >= 7 and legal > 20 → 3. Don't reduce when gives check — skipping the check test (accepted). - Check extension: in_check → depth += 1. - Qsearch: delta margin 200. - TT depth-preferred replace. - killers/history as planned. Also — one more guard in negamax: when depth-1-red < 0 could go negative → qsearch handles depth <= 0 ✓ (negamax with depth <= 0 → qsearch ✓ since check `if depth <= 0` before extension... wait order: `if depth <= 0: return qsearch` then `if in_check: depth += 1` — at depth <= 0 we never reach extension ✓. NMP calls negamax(depth-1-R) possibly ≤ 0 → qsearch ✓ fine. Hmm — NMP when depth-1-R <= 0 → qsearch-based null ✓ fine (standard allows). Now — about move loop "quiet" determination: ```python cap = undo[2] != 0 or flag == 1 promo != 0 quiet = not cap and not promo ``` For history/killer updates on cutoff: only if quiet ✓. History index: base = 0 if mover white... mover = side before make. In negamax loop, before make: side = pos.side (mover). Compute base before loop. Wait — actually in the loop I make the move then evaluate; mover known before make: mover = pos.side at loop start... pos.side changes after make; capture mover first: `side = pos.side` before loop ✓. Now let me also make sure about the alpha/beta bookkeeping in the futility `continue` path — we unmake and continue without affecting best ✓ (futility may return a wrong-low evaluation of node? Futility pruning skips moves when we're confident none will raise alpha; if ALL moves pruned, best remains -INF... then `if legal == 0` check — legal counts moves actually searched... futility-pruned moves already incremented `legal` (they were legal, made, then unmade). Hmm — in my loop I increment legal after legality check, then prune by futility — so legal > 0 → we don't hit the "no legal moves" mate branch ✓ (pruned moves were legal). But `best` stays -INF if all moves futility-pruned! Then we return best = -INF as node score — WRONG (score should be ≥ alpha... it's a fail-low: best < alpha means "upper bound = alpha"? Fail-soft with no moves searched → best should be... we must not return -INF (mate-ish!). Fix: initialize best = alpha? Standard: fail-soft returns max score found; if none found, return... Common implementation: keep `best = alpha` for upper bound (fail-hard-ish) or guard: `if best == -INF_BOUND: best = alpha`? Hmm — but alpha was possibly raised... no, if all pruned, alpha unchanged from entry. Safest: initialize `best = -INF_BOUND`; at the end, `if best == -INF_BOUND: best = alpha` (all moves pruned/illegal-but-counted... careful: "legal == 0" case handled separately (mate/stalemate) — but futility could prune all legal moves → return alpha (upper bound) ✓ sound-ish (we believe nothing exceeds alpha... futility says quiet moves don't gain enough; unsearched — returning alpha as bound is the standard "pruned to alpha"). Actually the standard implementations set bestScore = alpha initially (fail-hard) — I'll initialize `best = alpha` hmm but then flag for TT: best <= alpha0 → UPPER with value alpha — fine. But careful: if beta cutoff occurs, best updated properly ✓. But one nuance: `best = alpha` initial → "best >= beta" checks? Cutoff only inside loop ✓. TT store: flag = LOWER if best >= beta else UPPER if best <= alpha0... with best initialized alpha: if alpha == alpha0 (no move improved): best == alpha0 → flag UPPER value alpha0 ✓ fine. If some move improved alpha (not to beta): best > alpha0 → EXACT ✓. If alpha raised then futility pruned rest: best = alpha (raised) > alpha0 ✓ EXACT — correct? Score = max of searched = alpha raised; remaining quiets pruned believed ≤ alpha ✓ so node true score ≈ best ✓ EXACT fine (standard futility semantics). Edge: LMR re-search can return score > alpha... normal. But careful — initializing best = alpha breaks the "no legal moves" detection? `legal` counter separate ✓ untouched. Also mate/stalemate: legal == 0 → return -MATE+ply or 0 ✓ before TT store ✓. And best_move = 0 when nothing searched → TT store move 0 — guard: `if best_move:` store move... store anyway with 0 (harmless) — better skip move. Fine: store tuple (depth, flag, score, best_move) with best_move possibly 0 → ordering handles (mv == tt_move never matches since our moves ≠ 0) ✓. Now — another subtle fail: aspiration re-search at root after fail-high: my aspiration loop widens beta only; but the score from fail-high search may have raised `a` mid-search — irrelevant, re-search fresh ✓. Root search must not use the futility/pruning logic obviously (it calls negamax children) ✓. TT store at root? Don't store root (ply 0) — children store ✓. But PV extraction needs TT entries for root children reachable from root hash... PV extraction: from root position, look up root hash → not stored → e = None → PV empty! Fix: store root in TT after each iteration: `self.tt[pos.hash] = (depth, EXACT-ish, score, best)` — store with the iteration's depth/score/best ✓. Then PV extraction works. Implement in _think after each stable iteration: store (depth, 0 if in-window... after aspiration converges the score is in-window → EXACT, move best) ✓. PV line then: root → best → TT chain

Make a chess engine that use UCI +Alpha-beta pruning +Minim...