chenmoranzhiandCursor
06eb360fbf
后续改内容或结算顺序时有冻结依据，不必再翻原始稿才能对齐。 Co-authored-by: Cursor <cursoragent@cursor.com>
41 lines
1.4 KiB
Markdown
41 lines
1.4 KiB
Markdown
# AI
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AI v1 consumes `Game.Core`. It does not reimplement rules.
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## Scope
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- Own current turn only
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- Not: MCTS, minimax, multi-turn planning, predicting the player’s next turn, predicting player responses, LLM play
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Pipeline: legal actions → simulate (`Clone` + `Execute`) → evaluate → shallow beam search → near-optimal pick.
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Implemented in `TurnBeamSearch` (Game.Core). Playtest client shows each AI step’s top scored candidates.
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## Core loop
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```
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state
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→ GetLegalActions
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→ simulate each action (CloneState + Execute)
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→ evaluate
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→ keep best candidates (beam width)
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→ expand
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→ stop at EndTurn or depth limit
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```
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After each **real** action, search again. Do not search the opponent’s next turn.
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## Evaluation terms
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`PlayerDamage`, `EnemyUnitRemoved`, `OwnBoardValue`, `EnemyBoardValue`, `Heal`, `Draw`, `Energy`, `HandValue`, `CountdownValue`, `StatusValue`.
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Killing the player: `WIN_SCORE` far above ordinary terms. Weights per enemy and board (e.g. more draw weight on empty hand). Do not show weights in UI.
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## Near-optimal randomness
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Compute `BestScore`. Sample uniformly (via `GameRng`) among lines with `Score >= BestScore * Threshold`. Avoids both obvious blunders and fully deterministic puppeteering.
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## Performance
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Limit beam width and action depth. Dedupe identical/equivalent states. Do not simulate player responses in v1. Deep-copy until clone/`Execute` is the measured bottleneck.
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