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176 wiersze
4.9 KiB
176 wiersze
4.9 KiB
-module(ai_core).
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-export([init_ai/1, make_decision/2, update_strategy/3]).
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-record(ai_state, {
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personality, % aggressive | conservative | balanced
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strategy_weights, % 策略权重
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knowledge_base, % 知识库
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game_history = [] % 游戏历史
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}).
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%% AI初始化
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init_ai(Personality) ->
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#ai_state{
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personality = Personality,
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strategy_weights = init_weights(Personality),
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knowledge_base = init_knowledge_base()
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}.
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%% 决策制定
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make_decision(AIState, GameState) ->
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% 分析当前局势
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Situation = analyze_situation(GameState),
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% 生成可能的行动
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PossiblePlays = generate_possible_plays(GameState),
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% 评估每个行动
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RatedPlays = evaluate_plays(PossiblePlays, AIState, Situation),
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% 选择最佳行动
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select_best_play(RatedPlays, AIState).
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%% 策略更新
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update_strategy(AIState, GameResult, GameHistory) ->
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NewWeights = adjust_weights(AIState#ai_state.strategy_weights, GameResult),
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NewKnowledge = update_knowledge(AIState#ai_state.knowledge_base, GameHistory),
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AIState#ai_state{
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strategy_weights = NewWeights,
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knowledge_base = NewKnowledge,
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game_history = [GameHistory | AIState#ai_state.game_history]
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}.
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%% 内部函数
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init_weights(aggressive) ->
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#{
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control_weight => 0.8,
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attack_weight => 0.7,
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defense_weight => 0.3,
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risk_weight => 0.6
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};
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init_weights(conservative) ->
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#{
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control_weight => 0.5,
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attack_weight => 0.4,
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defense_weight => 0.8,
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risk_weight => 0.3
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};
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init_weights(balanced) ->
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#{
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control_weight => 0.6,
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attack_weight => 0.6,
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defense_weight => 0.6,
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risk_weight => 0.5
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}.
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analyze_situation(GameState) ->
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#{
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hand_strength => evaluate_hand_strength(GameState),
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control_status => evaluate_control(GameState),
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opponent_cards => estimate_opponent_cards(GameState),
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game_stage => determine_game_stage(GameState)
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}.
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generate_possible_plays(GameState) ->
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MyCards = get_my_cards(GameState),
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LastPlay = get_last_play(GameState),
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generate_valid_plays(MyCards, LastPlay).
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evaluate_plays(Plays, AIState, Situation) ->
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lists:map(
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fun(Play) ->
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Score = calculate_play_score(Play, AIState, Situation),
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{Play, Score}
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end,
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Plays
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).
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calculate_play_score(Play, AIState, Situation) ->
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Weights = AIState#ai_state.strategy_weights,
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ControlScore = evaluate_control_value(Play, Situation) *
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maps:get(control_weight, Weights),
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AttackScore = evaluate_attack_value(Play, Situation) *
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maps:get(attack_weight, Weights),
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DefenseScore = evaluate_defense_value(Play, Situation) *
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maps:get(defense_weight, Weights),
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RiskScore = evaluate_risk_value(Play, Situation) *
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maps:get(risk_weight, Weights),
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ControlScore + AttackScore + DefenseScore + RiskScore.
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select_best_play(RatedPlays, AIState) ->
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case AIState#ai_state.personality of
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aggressive ->
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select_aggressive(RatedPlays);
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conservative ->
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select_conservative(RatedPlays);
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balanced ->
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select_balanced(RatedPlays)
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end.
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%% 策略选择函数
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select_aggressive(RatedPlays) ->
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% 倾向于选择得分最高的行动
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{Play, _Score} = lists:max(RatedPlays),
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Play.
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select_conservative(RatedPlays) ->
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% 倾向于选择风险较低的行动
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SafePlays = filter_safe_plays(RatedPlays),
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case SafePlays of
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[] -> select_balanced(RatedPlays);
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_ -> select_from_safe_plays(SafePlays)
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end.
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select_balanced(RatedPlays) ->
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% 在得分和风险之间寻找平衡
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{Play, _Score} = select_balanced_play(RatedPlays),
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Play.
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%% 评估函数
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evaluate_hand_strength(GameState) ->
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Cards = get_my_cards(GameState),
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calculate_hand_value(Cards).
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evaluate_control(GameState) ->
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% 评估是否控制局势
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LastPlay = get_last_play(GameState),
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MyCards = get_my_cards(GameState),
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can_control_game(MyCards, LastPlay).
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estimate_opponent_cards(GameState) ->
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% 基于已出牌情况估计对手手牌
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PlayedCards = get_played_cards(GameState),
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MyCards = get_my_cards(GameState),
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estimate_remaining_cards(PlayedCards, MyCards).
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%% 知识库更新
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update_knowledge(KnowledgeBase, GameHistory) ->
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% 更新AI的知识库
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NewPatterns = extract_patterns(GameHistory),
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merge_knowledge(KnowledgeBase, NewPatterns).
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extract_patterns(GameHistory) ->
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% 从游戏历史中提取出牌模式
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lists:foldl(
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fun(Play, Patterns) ->
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Pattern = analyze_play_pattern(Play),
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update_pattern_stats(Pattern, Patterns)
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end,
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#{},
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GameHistory
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).
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merge_knowledge(Old, New) ->
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maps:merge_with(
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fun(_Key, OldValue, NewValue) ->
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update_knowledge_value(OldValue, NewValue)
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end,
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Old,
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New
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).
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