cardSrv/src/ai_core.erl

-module(ai_core).
-export([init_ai/1, make_decision/2, update_strategy/3]).

-record(ai_state, {
    personality,      % aggressive | conservative | balanced
    strategy_weights, % 策略权重
    knowledge_base,   % 知识库
    game_history = [] % 游戏历史
}).

%% AI初始化
init_ai(Personality) ->
    #ai_state{
        personality = Personality,
        strategy_weights = init_weights(Personality),
        knowledge_base = init_knowledge_base()
    }.

%% 决策制定
make_decision(AIState, GameState) ->
    % 分析当前局势
    Situation = analyze_situation(GameState),
    
    % 生成可能的行动
    PossiblePlays = generate_possible_plays(GameState),
    
    % 评估每个行动
    RatedPlays = evaluate_plays(PossiblePlays, AIState, Situation),
    
    % 选择最佳行动
    select_best_play(RatedPlays, AIState).

%% 策略更新
update_strategy(AIState, GameResult, GameHistory) ->
    NewWeights = adjust_weights(AIState#ai_state.strategy_weights, GameResult),
    NewKnowledge = update_knowledge(AIState#ai_state.knowledge_base, GameHistory),
    AIState#ai_state{
        strategy_weights = NewWeights,
        knowledge_base = NewKnowledge,
        game_history = [GameHistory | AIState#ai_state.game_history]
    }.

%% 内部函数

init_weights(aggressive) ->
    #{
        control_weight => 0.8,
        attack_weight => 0.7,
        defense_weight => 0.3,
        risk_weight => 0.6
    };
init_weights(conservative) ->
    #{
        control_weight => 0.5,
        attack_weight => 0.4,
        defense_weight => 0.8,
        risk_weight => 0.3
    };
init_weights(balanced) ->
    #{
        control_weight => 0.6,
        attack_weight => 0.6,
        defense_weight => 0.6,
        risk_weight => 0.5
    }.

analyze_situation(GameState) ->
    #{
        hand_strength => evaluate_hand_strength(GameState),
        control_status => evaluate_control(GameState),
        opponent_cards => estimate_opponent_cards(GameState),
        game_stage => determine_game_stage(GameState)
    }.

generate_possible_plays(GameState) ->
    MyCards = get_my_cards(GameState),
    LastPlay = get_last_play(GameState),
    generate_valid_plays(MyCards, LastPlay).

evaluate_plays(Plays, AIState, Situation) ->
    lists:map(
        fun(Play) ->
            Score = calculate_play_score(Play, AIState, Situation),
            {Play, Score}
        end,
        Plays
    ).

calculate_play_score(Play, AIState, Situation) ->
    Weights = AIState#ai_state.strategy_weights,
    
    ControlScore = evaluate_control_value(Play, Situation) * 
                  maps:get(control_weight, Weights),
    AttackScore = evaluate_attack_value(Play, Situation) * 
                 maps:get(attack_weight, Weights),
    DefenseScore = evaluate_defense_value(Play, Situation) * 
                  maps:get(defense_weight, Weights),
    RiskScore = evaluate_risk_value(Play, Situation) * 
                maps:get(risk_weight, Weights),
    
    ControlScore + AttackScore + DefenseScore + RiskScore.

select_best_play(RatedPlays, AIState) ->
    case AIState#ai_state.personality of
        aggressive -> 
            select_aggressive(RatedPlays);
        conservative -> 
            select_conservative(RatedPlays);
        balanced -> 
            select_balanced(RatedPlays)
    end.

%% 策略选择函数

select_aggressive(RatedPlays) ->
    % 倾向于选择得分最高的行动
    {Play, _Score} = lists:max(RatedPlays),
    Play.

select_conservative(RatedPlays) ->
    % 倾向于选择风险较低的行动
    SafePlays = filter_safe_plays(RatedPlays),
    case SafePlays of
        [] -> select_balanced(RatedPlays);
        _ -> select_from_safe_plays(SafePlays)
    end.

select_balanced(RatedPlays) ->
    % 在得分和风险之间寻找平衡
    {Play, _Score} = select_balanced_play(RatedPlays),
    Play.

%% 评估函数

evaluate_hand_strength(GameState) ->
    Cards = get_my_cards(GameState),
    calculate_hand_value(Cards).

evaluate_control(GameState) ->
    % 评估是否控制局势
    LastPlay = get_last_play(GameState),
    MyCards = get_my_cards(GameState),
    can_control_game(MyCards, LastPlay).

estimate_opponent_cards(GameState) ->
    % 基于已出牌情况估计对手手牌
    PlayedCards = get_played_cards(GameState),
    MyCards = get_my_cards(GameState),
    estimate_remaining_cards(PlayedCards, MyCards).

%% 知识库更新

update_knowledge(KnowledgeBase, GameHistory) ->
    % 更新AI的知识库
    NewPatterns = extract_patterns(GameHistory),
    merge_knowledge(KnowledgeBase, NewPatterns).

extract_patterns(GameHistory) ->
    % 从游戏历史中提取出牌模式
    lists:foldl(
        fun(Play, Patterns) ->
            Pattern = analyze_play_pattern(Play),
            update_pattern_stats(Pattern, Patterns)
        end,
        #{},
        GameHistory
    ).

merge_knowledge(Old, New) ->
    maps:merge_with(
        fun(_Key, OldValue, NewValue) ->
            update_knowledge_value(OldValue, NewValue)
        end,
        Old,
        New
    ).