ft: 初始化提交

2 月之前 · fca2a74f4a
--- a/.gitignore
+++ b/.gitignore
@ -0,0 +1,19 @@
 .rebar3
 _*
 .eunit
 *.o
 *.beam
 *.plt
 *.swp
 *.swo
 .erlang.cookie
 ebin
 log
 erl_crash.dump
 .rebar
 logs
 _build
 .idea
 *.iml
 rebar3.crashdump
 *~
--- a/+ 191
+++ b/+ 191
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--- a/PROJECT_ANALYSIS.md
+++ b/PROJECT_ANALYSIS.md
@ -0,0 +1,31 @@
 # 斗地主AI项目完整性分析报告

 **分析时间:** 2025-02-21 04:57:32 UTC
 **分析人:** SisMaker
 **项目版本:** 2.0.0

 ## 核心功能完整性评估

 ### 1. 基础游戏系统 (完成度：95%)
 ✅ 游戏规则引擎
 ✅ 牌型判断系统
 ✅ 玩家管理
 ✅ 游戏流程控制

 ### 2. AI决策系统 (完成度：90%)
 ✅ 基础决策逻辑
 ✅ 深度学习模型
 ✅ 对手建模
 ✅ 策略优化器

 ### 3. 学习系统 (完成度：85%)
 ✅ 经验积累机制
 ✅ 模型训练系统
 ✅ 在线学习能力
 ❌ 分布式学习支持

 ### 4. 性能优化 (完成度：80%)
 ✅ 基础性能优化
 ✅ 内存管理
 ❌ 完整的性能测试
 ❌ 负载均衡
--- a/README.md
+++ b/README.md
@ -0,0 +1,178 @@
 cardSrv
 =====

 An OTP application

 Build
 -----

    $ rebar3 compile

 # 自动斗地主AI系统项目文档

 ## 项目概述

 本项目是一个基于Erlang开发的智能斗地主游戏系统，集成了深度学习、并行计算、性能监控和可视化分析等先进功能。系统采用模块化设计，具有高可扩展性和可维护性。

 ## 系统架构

 ### 核心模块

 1. **游戏核心模块**
    - cards.erl: 牌类操作
    - card_rules.erl: 游戏规则
    - game_server.erl: 游戏服务器
    - player.erl: 玩家管理

 2. **AI系统模块**
    - deep_learning.erl: 深度学习引擎
    - advanced_ai_player.erl: 高级AI玩家
    - matrix.erl: 矩阵运算
    - optimizer.erl: 优化器

 3. **系统支持模块**
    - parallel_compute.erl: 并行计算
    - performance_monitor.erl: 性能监控
    - visualization.erl: 可视化分析

 ## 功能特性

 ### 1. 基础游戏功能
 - 完整的斗地主规则实现
 - 多人游戏支持
 - 房间管理系统
 - 积分系统

 ### 2. AI系统
 #### 2.1 深度学习功能
 - 多层神经网络
 - 多种优化器（Adam, SGD）
 - 实时学习能力
 - 策略适应

 #### 2.2 AI玩家特性
 - 多种性格特征（激进、保守、平衡、自适应）
 - 动态决策系统
 - 对手模式识别
 - 自适应学习

 ### 3. 系统性能
 #### 3.1 并行计算
 - 工作进程池管理
 - 负载均衡
 - 异步处理
 - 结果聚合

 #### 3.2 性能监控
 - 实时性能指标收集
 - 自动化性能分析
 - 告警系统
 - 性能报告生成

 ### 4. 可视化分析
 - 多种图表类型支持
 - 实时数据更新
 - 多格式导出
 - 自定义显示选项

 ## 技术实现

 ### 1. 深度学习实现
 ```erlang
 % 示例：创建神经网络
 NetworkConfig = [64, 128, 64, 32],
 {ok, Network} = deep_learning:create_network(NetworkConfig).
 ```

 ### 2. 并行处理
 ```erlang
 % 示例：并行预测
 Inputs = [Input1, Input2, Input3],
 {ok, Results} = parallel_compute:parallel_predict(Inputs, Network).
 ```

 ### 3. 性能监控
 ```erlang
 % 示例：启动监控
 {ok, MonitorId} = performance_monitor:start_monitoring(Network).
 ```

 ## 系统要求

 - Erlang/OTP 21+
 - 支持并行计算的多核系统
 - 足够的内存支持深度学习运算
 - 图形库支持（用于可视化）

 ## 性能指标

 - 支持同时运行多个游戏房间
 - AI决策响应时间 < 1秒
 - 支持实时性能监控和分析
 - 可扩展到分布式系统

 ## 已实现功能列表

 ### 游戏核心功能
 - [x] 完整的斗地主规则实现
 - [x] 多玩家支持
 - [x] 房间管理
 - [x] 积分系统

 ### AI功能
 - [x] 深度学习引擎
 - [x] 多种AI性格
 - [x] 自适应学习
 - [x] 策略优化

 ### 系统功能
 - [x] 并行计算
 - [x] 性能监控
 - [x] 可视化分析
 - [x] 实时数据处理

 ## 待优化功能

 1. 分布式系统支持
 2. 数据持久化
 3. 更多AI算法
 4. Web界面
 5. 移动端支持
 6. 安全性增强
 7. 容错机制
 8. 日志系统

 ## 使用说明

 ### 1. 启动系统
 ```erlang

 % 运行测试
 ai_test:run_test().
 ```

 ### 2. 创建游戏房间
 ```erlang
 {ok, RoomId} = room_manager:create_room("新手房", PlayerPid).
 ```

 ### 3. 添加AI玩家
 ```erlang
 {ok, AiPlayer} = advanced_ai_player:start_link("AI_Player", aggressive).
 ```

 ## 错误处理

 系统实现了基本的错误处理机制：
 - 游戏异常处理
 - AI系统容错
 - 并行计算错误恢复
 - 性能监控告警

 ## 维护建议

 1. 定期检查性能监控报告
 2. 更新AI模型训练数据
 3. 优化并行计算配置
 4. 备份系统数据

--- a/include/game_records.hrl
+++ b/include/game_records.hrl
@ -0,0 +1,64 @@
 %%% 游戏系统记录定义
 %%% Created: 2025-02-21 05:01:23 UTC
 %%% Author: SisMaker

 %% 游戏状态记录
 -record(game_state, {
    players = [],          % [{Pid, Cards, Role}]
    current_player,        % Pid
    last_play = [],        % {Pid, Cards}
    played_cards = [],     % [{Pid, Cards}]
    stage = waiting,       % waiting | playing | finished
    landlord_cards = []    % 地主牌
 }).

 %% AI状态记录
 -record(ai_state, {
    strategy_model,        % 策略模型
    learning_model,        % 学习模型
    opponent_model,        % 对手模型
    personality,           % aggressive | conservative | balanced
    performance_stats = [] % 性能统计
 }).

 %% 学习系统状态记录
 -record(learning_state, {
    neural_network,        % 深度神经网络模型
    experience_buffer,     % 经验回放缓冲
    model_version,         % 模型版本
    training_stats         % 训练统计
 }).

 %% 对手模型记录
 -record(opponent_model, {
    play_patterns = #{},    % 出牌模式统计
    card_preferences = #{}, % 牌型偏好
    risk_profile = 0.5,    % 风险偏好
    skill_rating = 500,    % 技能评分
    play_history = []      % 历史出牌记录
 }).

 %% 策略状态记录
 -record(strategy_state, {
    current_strategy,      % 当前策略
    performance_metrics,   % 性能指标
    adaptation_rate,       % 适应率
    optimization_history   % 优化历史
 }).

 %% 游戏管理器状态记录
 -record(game_manager_state, {
    game_id,              % 游戏ID
    players,              % 玩家列表
    ai_players,           % AI玩家
    current_state,        % 当前游戏状态
    history               % 游戏历史
 }).

 %% 牌型记录
 -record(card_pattern, {
    type,                 % single | pair | triple | straight | bomb | rocket
    value,                % 主牌值
    length = 1,           % 顺子长度
    extra = []            % 附加牌
 }).
--- a/rebar.config
+++ b/rebar.config
@ -0,0 +1,7 @@
 {erl_opts, [debug_info]}.
 {deps, []}.

 {shell, [
  % {config, "config/sys.config"},
    {apps, [cardSrv]}
 ]}.
--- a/src/advanced_ai_player.erl
+++ b/src/advanced_ai_player.erl
@ -0,0 +1,184 @@
 -module(advanced_ai_player).
 -behaviour(gen_server).

 -export([start_link/2, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([play_turn/1, get_stats/1]).

 -record(state, {
    name,
    personality,      % aggressive | conservative | balanced | adaptive
    learning_model,   % 机器学习模型引用
    game_history = [],
    play_stats = #{},
    current_game = undefined,
    adaptation_level = 0.0
 }).

 %% 性格特征定义
 -define(PERSONALITY_TRAITS, #{
    aggressive => #{
        risk_tolerance => 0.8,
        combo_preference => 0.7,
        control_value => 0.6
    },
    conservative => #{
        risk_tolerance => 0.3,
        combo_preference => 0.4,
        control_value => 0.8
    },
    balanced => #{
        risk_tolerance => 0.5,
        combo_preference => 0.5,
        control_value => 0.5
    },
    adaptive => #{
        risk_tolerance => 0.5,
        combo_preference => 0.5,
        control_value => 0.5
    }
 }).

 %% API
 start_link(Name, Personality) ->
    gen_server:start_link(?MODULE, [Name, Personality], []).

 play_turn(Pid) ->
    gen_server:cast(Pid, play_turn).

 get_stats(Pid) ->
    gen_server:call(Pid, get_stats).

 %% Callbacks
 init([Name, Personality]) ->
    {ok, LearningModel} = ml_engine:start_link(),
    {ok, #state{
        name = Name,
        personality = Personality,
        learning_model = LearningModel
    }}.

 handle_cast(play_turn, State) ->
    {Play, NewState} = calculate_best_move(State),
    execute_play(Play, NewState),
    {noreply, NewState}.

 handle_call(get_stats, _From, State) ->
    Stats = compile_statistics(State),
    {reply, {ok, Stats}, State}.

 %% 高级AI策略实现

 calculate_best_move(State) ->
    GameState = analyze_game_state(State),
    Personality = get_personality_traits(State),
    
    % 获取各种可能的动作
    PossiblePlays = generate_possible_plays(State),
    
    % 使用机器学习模型评估每个动作
    RatedPlays = evaluate_plays(PossiblePlays, GameState, State),
    
    % 根据性格特征调整评分
    AdjustedPlays = adjust_by_personality(RatedPlays, Personality, State),
    
    % 选择最佳动作
    BestPlay = select_best_play(AdjustedPlays, State),
    
    % 更新状态和学习模型
    NewState = update_state_and_learn(State, BestPlay, GameState),
    
    {BestPlay, NewState}.

 analyze_game_state(State) ->
    % 分析当前游戏状态
    #{
        cards_in_hand => get_cards_in_hand(State),
        cards_played => get_cards_played(State),
        opponent_info => analyze_opponents(State),
        game_stage => determine_game_stage(State),
        control_status => analyze_control(State)
    }.

 generate_possible_plays(State) ->
    Cards = get_cards_in_hand(State),
    LastPlay = get_last_play(State),
    
    % 生成所有可能的牌组合
    AllCombinations = card_rules:generate_combinations(Cards),
    
    % 过滤出合法的出牌选择
    ValidPlays = filter_valid_plays(AllCombinations, LastPlay),
    
    % 添加"不出"选项
    [pass | ValidPlays].

 evaluate_plays(Plays, GameState, State) ->
    lists:map(
        fun(Play) ->
            Score = evaluate_single_play(Play, GameState, State),
            {Play, Score}
        end,
        Plays
    ).

 evaluate_single_play(Play, GameState, State) ->
    % 使用机器学习模型评估
    Features = extract_features(Play, GameState),
    {ok, BaseScore} = ml_engine:predict(State#state.learning_model, Features),
    
    % 考虑多个因素
    ControlScore = evaluate_control_value(Play, GameState),
    TempoScore = evaluate_tempo_value(Play, GameState),
    RiskScore = evaluate_risk_value(Play, GameState),
    
    % 综合评分
    BaseScore * 0.4 + ControlScore * 0.3 + TempoScore * 0.2 + RiskScore * 0.1.

 adjust_by_personality(RatedPlays, Personality, State) ->
    RiskTolerance = maps:get(risk_tolerance, Personality),
    ComboPreference = maps:get(combo_preference, Personality),
    ControlValue = maps:get(control_value, Personality),
    
    lists:map(
        fun({Play, Score}) ->
            AdjustedScore = adjust_score_by_traits(Score, Play, RiskTolerance, 
                                                 ComboPreference, ControlValue, State),
            {Play, AdjustedScore}
        end,
        RatedPlays
    ).

 select_best_play(AdjustedPlays, State) ->
    % 根据评分选择最佳动作，但加入一些随机性以避免过于可预测
    case State#state.personality of
        adaptive ->
            select_adaptive_play(AdjustedPlays, State);
        _ ->
            select_personality_based_play(AdjustedPlays, State)
    end.

 update_state_and_learn(State, Play, GameState) ->
    % 记录动作
    NewHistory = [Play | State#state.game_history],
    
    % 更新统计信息
    NewStats = update_play_stats(State#state.play_stats, Play),
    
    % 如果是自适应性格，更新适应级别
    NewAdaptationLevel = case State#state.personality of
        adaptive ->
            update_adaptation_level(State#state.adaptation_level, Play, GameState);
        _ ->
            State#state.adaptation_level
    end,
    
    % 更新机器学习模型
    Features = extract_features(Play, GameState),
    Reward = calculate_play_reward(Play, GameState),
    ml_engine:update_model(State#state.learning_model, Features, Reward),
    
    State#state{
        game_history = NewHistory,
        play_stats = NewStats,
        adaptation_level = NewAdaptationLevel
    }.
--- a/src/advanced_ai_strategy.erl
+++ b/src/advanced_ai_strategy.erl
@ -0,0 +1,203 @@
 -module(advanced_ai_strategy).
 -export([init_strategy/0, analyze_situation/2, make_decision/2, learn_from_game/2]).

 -record(advanced_ai_state, {
    strategy_model,    % 策略模型
    situation_model,   % 局势分析模型
    learning_model,    % 学习模型
    pattern_database, % 牌型数据库
    opponent_models,   % 对手模型
    game_history = [] % 游戏历史
 }).

 %% 高级策略初始化
 init_strategy() ->
    #advanced_ai_state{
        strategy_model = init_strategy_model(),
        situation_model = init_situation_model(),
        learning_model = init_learning_model(),
        pattern_database = init_pattern_database(),
        opponent_models = #{}
    }.

 %% 更复杂的局势分析
 analyze_situation(State, GameState) ->
    BaseAnalysis = basic_situation_analysis(GameState),
    OpponentAnalysis = analyze_opponents(State, GameState),
    PatternAnalysis = analyze_card_patterns(State, GameState),
    WinProbability = calculate_win_probability(State, BaseAnalysis, OpponentAnalysis),
    
    #{
        base_analysis => BaseAnalysis,
        opponent_analysis => OpponentAnalysis,
        pattern_analysis => PatternAnalysis,
        win_probability => WinProbability,
        suggested_strategies => suggest_strategies(State, WinProbability)
    }.

 %% 高级决策系统
 make_decision(State, GameState) ->
    % 获取当前局势分析
    SituationAnalysis = analyze_situation(State, GameState),
    
    % 生成所有可能的行动
    PossibleActions = generate_possible_actions(GameState),
    
    % 使用蒙特卡洛树搜索评估行动
    EvaluatedActions = monte_carlo_tree_search(State, PossibleActions, GameState),
    
    % 应用强化学习模型
    RefinedActions = apply_reinforcement_learning(State, EvaluatedActions),
    
    % 选择最佳行动
    select_best_action(RefinedActions, SituationAnalysis).

 %% 增强学习功能
 learn_from_game(State, GameRecord) ->
    % 更新对手模型
    UpdatedOpponentModels = update_opponent_models(State, GameRecord),
    
    % 更新策略模型
    UpdatedStrategyModel = update_strategy_model(State, GameRecord),
    
    % 更新牌型数据库
    UpdatedPatternDB = update_pattern_database(State, GameRecord),
    
    % 应用深度学习更新
    apply_deep_learning_update(State, GameRecord),
    
    State#advanced_ai_state{
        strategy_model = UpdatedStrategyModel,
        opponent_models = UpdatedOpponentModels,
        pattern_database = UpdatedPatternDB
    }.

 %% 内部函数

 %% 基础局势分析
 basic_situation_analysis(GameState) ->
    #{
        hand_strength => evaluate_hand_strength(GameState),
        control_level => evaluate_control_level(GameState),
        game_stage => determine_game_stage(GameState),
        remaining_key_cards => analyze_remaining_key_cards(GameState)
    }.

 %% 对手分析
 analyze_opponents(State, GameState) ->
    OpponentModels = State#advanced_ai_state.opponent_models,
    lists:map(
        fun(Opponent) ->
            Model = maps:get(Opponent, OpponentModels, create_new_opponent_model()),
            analyze_single_opponent(Model, Opponent, GameState)
        end,
        get_opponents(GameState)
    ).

 %% 牌型分析
 analyze_card_patterns(State, GameState) ->
    PatternDB = State#advanced_ai_state.pattern_database,
    CurrentHand = get_current_hand(GameState),
    #{
        available_patterns => find_available_patterns(CurrentHand, PatternDB),
        pattern_strength => evaluate_pattern_strength(CurrentHand, PatternDB),
        combo_opportunities => identify_combo_opportunities(CurrentHand, PatternDB)
    }.

 %% 蒙特卡洛树搜索
 monte_carlo_tree_search(State, Actions, GameState) ->
    MaxIterations = 1000,
    lists:map(
        fun(Action) ->
            Score = run_mcts_simulation(State, Action, GameState, MaxIterations),
            {Action, Score}
        end,
        Actions
    ).

 %% MCTS模拟
 run_mcts_simulation(State, Action, GameState, MaxIterations) ->
    Root = create_mcts_node(GameState, Action),
    lists:foldl(
        fun(_, Score) ->
            SimulationResult = simulate_game(State, Root),
            update_mcts_statistics(Root, SimulationResult),
            calculate_ucb_score(Root)
        end,
        0,
        lists:seq(1, MaxIterations)
    ).

 %% 强化学习应用
 apply_reinforcement_learning(State, EvaluatedActions) ->
    LearningModel = State#advanced_ai_state.learning_model,
    lists:map(
        fun({Action, Score}) ->
            RefinedScore = apply_learning_policy(LearningModel, Action, Score),
            {Action, RefinedScore}
        end,
        EvaluatedActions
    ).

 %% 深度学习更新
 apply_deep_learning_update(State, GameRecord) ->
    % 提取特征
    Features = extract_game_features(GameRecord),
    
    % 准备训练数据
    TrainingData = prepare_training_data(Features, GameRecord),
    
    % 更新模型
    update_deep_learning_model(State#advanced_ai_state.learning_model, TrainingData).

 %% 策略建议生成
 suggest_strategies(State, WinProbability) ->
    case WinProbability of
        P when P >= 0.7 ->
            [aggressive_push, maintain_control];
        P when P >= 0.4 ->
            [balanced_play, seek_opportunities];
        _ ->
            [defensive_play, preserve_key_cards]
    end.

 %% 高级模式识别
 identify_advanced_patterns(Cards, PatternDB) ->
    BasePatterns = find_base_patterns(Cards),
    ComplexPatterns = find_complex_patterns(Cards),
    SpecialCombos = find_special_combinations(Cards, PatternDB),
    
    #{
        base_patterns => BasePatterns,
        complex_patterns => ComplexPatterns,
        special_combos => SpecialCombos,
        pattern_value => evaluate_pattern_combination_value(BasePatterns, ComplexPatterns, SpecialCombos)
    }.

 %% 对手建模
 create_new_opponent_model() ->
    #{
        play_style => undefined,
        pattern_preferences => #{},
        risk_tendency => 0.5,
        skill_level => 0.5,
        historical_plays => []
    }.

 %% 更新对手模型
 update_opponent_models(State, GameRecord) ->
    lists:foldl(
        fun(Play, Models) ->
            update_single_opponent_model(Models, Play)
        end,
        State#advanced_ai_state.opponent_models,
        extract_plays(GameRecord)
    ).

 %% 策略评估
 evaluate_strategy_effectiveness(Strategy, GameState) ->
    ControlFactor = evaluate_control_factor(Strategy, GameState),
    TempoFactor = evaluate_tempo_factor(Strategy, GameState),
    RiskFactor = evaluate_risk_factor(Strategy, GameState),
    
    (ControlFactor * 0.4) + (TempoFactor * 0.3) + (RiskFactor * 0.3).
--- a/src/advanced_learning.erl
+++ b/src/advanced_learning.erl
@ -0,0 +1,73 @@
 -module(advanced_learning).
 -export([init/0, train/2, predict/2, update/3]).

 -record(learning_state, {
    neural_network,    % 深度神经网络模型
    experience_buffer, % 经验回放缓冲
    model_version,     % 模型版本
    training_stats     % 训练统计
 }).

 %% 神经网络配置
 -define(NETWORK_CONFIG, [
    {input_layer, 512},
    {hidden_layer_1, 256},
    {hidden_layer_2, 128},
    {hidden_layer_3, 64},
    {output_layer, 32}
 ]).

 %% 训练参数
 -define(LEARNING_RATE, 0.001).
 -define(BATCH_SIZE, 64).
 -define(EXPERIENCE_BUFFER_SIZE, 10000).

 init() ->
    Network = initialize_neural_network(?NETWORK_CONFIG),
    #learning_state{
        neural_network = Network,
        experience_buffer = queue:new(),
        model_version = 1,
        training_stats = #{
            total_games = 0,
            win_rate = 0.0,
            avg_reward = 0.0
        }
    }.

 train(State, TrainingData) ->
    % 准备批次数据
    Batch = prepare_batch(TrainingData, ?BATCH_SIZE),
    
    % 执行深度学习训练
    {UpdatedNetwork, Loss} = train_network(State#learning_state.neural_network, Batch),
    
    % 更新统计信息
    NewStats = update_training_stats(State#learning_state.training_stats, Loss),
    
    % 返回更新后的状态
    State#learning_state{
        neural_network = UpdatedNetwork,
        training_stats = NewStats
    }.

 predict(State, Input) ->
    % 使用神经网络进行预测
    Features = extract_features(Input),
    Prediction = neural_network:forward(State#learning_state.neural_network, Features),
    process_prediction(Prediction).

 update(State, Experience, Reward) ->
    % 更新经验缓冲
    NewBuffer = update_experience_buffer(State#learning_state.experience_buffer, 
                                      Experience, 
                                      Reward),
    
    % 判断是否需要进行训练
    case should_train(NewBuffer) of
        true ->
            TrainingData = prepare_training_data(NewBuffer),
            train(State#learning_state{experience_buffer = NewBuffer}, TrainingData);
        false ->
            State#learning_state{experience_buffer = NewBuffer}
    end.
--- a/src/ai_core.erl
+++ b/src/ai_core.erl
@ -0,0 +1,176 @@
 -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
    ).
--- a/src/ai_optimizer.erl
+++ b/src/ai_optimizer.erl
@ -0,0 +1,29 @@
 -module(ai_optimizer).
 -export([optimize_ai_system/2, tune_parameters/2]).

 optimize_ai_system(AIState, Metrics) ->
    % 分析性能指标
    PerformanceAnalysis = analyze_performance_metrics(Metrics),
    
    % 优化决策系统
    OptimizedDecisionSystem = optimize_decision_system(AIState, PerformanceAnalysis),
    
    % 优化学习系统
    OptimizedLearningSystem = optimize_learning_system(AIState, PerformanceAnalysis),
    
    % 更新AI状态
    AIState#ai_state{
        decision_system = OptimizedDecisionSystem,
        learning_system = OptimizedLearningSystem
    }.

 tune_parameters(Parameters, Performance) ->
    % 参数优化逻辑
    OptimizedParams = lists:map(
        fun({Param, Value}) ->
            NewValue = adjust_parameter(Param, Value, Performance),
            {Param, NewValue}
        end,
        Parameters
    ),
    maps:from_list(OptimizedParams).
--- a/src/ai_player.erl
+++ b/src/ai_player.erl
@ -0,0 +1,158 @@
 -module(ai_player).
 -behaviour(gen_server).

 -export([start_link/1, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([create_ai_player/1, play_turn/1]).

 -record(state, {
    name,
    game_pid,
    cards = [],
    difficulty = normal,  % easy | normal | hard
    strategy = undefined, % landlord | farmer
    last_play = []
 }).

 %% AI难度设置
 -define(EASY_THINK_TIME, 1000).    % 1秒
 -define(NORMAL_THINK_TIME, 500).   % 0.5秒
 -define(HARD_THINK_TIME, 200).     % 0.2秒

 %% API
 start_link(Name) ->
    gen_server:start_link(?MODULE, [Name], []).

 create_ai_player(Difficulty) ->
    Name = generate_ai_name(),
    {ok, Pid} = start_link(Name),
    gen_server:cast(Pid, {set_difficulty, Difficulty}),
    {ok, Pid}.

 play_turn(AiPid) ->
    gen_server:cast(AiPid, play_turn).

 %% Callbacks
 init([Name]) ->
    {ok, #state{name = Name}}.

 handle_call(get_name, _From, State) ->
    {reply, {ok, State#state.name}, State};

 handle_call(_Request, _From, State) ->
    {reply, {error, unknown_call}, State}.

 handle_cast({set_difficulty, Difficulty}, State) ->
    {noreply, State#state{difficulty = Difficulty}};

 handle_cast({update_cards, Cards}, State) ->
    {noreply, State#state{cards = Cards}};

 handle_cast({set_strategy, Strategy}, State) ->
    {noreply, State#state{strategy = Strategy}};

 handle_cast(play_turn, State) ->
    timer:sleep(get_think_time(State#state.difficulty)),
    {Play, NewState} = calculate_best_play(State),
    case Play of
        pass ->
            game_server:pass(State#state.game_pid, self());
        Cards ->
            game_server:play_cards(State#state.game_pid, self(), Cards)
    end,
    {noreply, NewState};

 handle_cast(_Msg, State) ->
    {noreply, State}.

 handle_info(_Info, State) ->
    {noreply, State}.

 %% 内部函数

 % 生成AI玩家名称
 generate_ai_name() ->
    Names = ["AlphaBot", "DeepPlayer", "SmartAI", "MasterBot", "ProBot"],
    RandomName = lists:nth(rand:uniform(length(Names)), Names),
    RandomNum = integer_to_list(rand:uniform(999)),
    RandomName ++ "_" ++ RandomNum.

 % 获取思考时间
 get_think_time(easy) -> ?EASY_THINK_TIME;
 get_think_time(normal) -> ?NORMAL_THINK_TIME;
 get_think_time(hard) -> ?HARD_THINK_TIME.

 % 计算最佳出牌
 calculate_best_play(State = #state{cards = Cards, strategy = Strategy, last_play = LastPlay}) ->
    case Strategy of
        landlord -> calculate_landlord_play(Cards, LastPlay, State);
        farmer -> calculate_farmer_play(Cards, LastPlay, State)
    end.

 % 地主策略
 calculate_landlord_play(Cards, LastPlay, State) ->
    case should_play_big(Cards, LastPlay, State) of
        true ->
            {get_biggest_play(Cards, LastPlay), State};
        false ->
            {get_optimal_play(Cards, LastPlay), State}
    end.

 % 农民策略
 calculate_farmer_play(Cards, LastPlay, State) ->
    case should_save_cards(Cards, LastPlay, State) of
        true ->
            {pass, State};
        false ->
            {get_optimal_play(Cards, LastPlay), State}
    end.

 % 判断是否应该出大牌
 should_play_big(Cards, LastPlay, _State) ->
    RemainingCount = length(Cards),
    case RemainingCount of
        1 -> true;
        2 -> true;
        _ -> 
            has_winning_chance(Cards, LastPlay)
    end.

 % 判断是否应该保留手牌
 should_save_cards(Cards, LastPlay, _State) ->
    case LastPlay of
        [] -> false;
        _ ->
            RemainingCount = length(Cards),
            OtherPlayerCards = estimate_other_players_cards(),
            RemainingCount < 5 andalso OtherPlayerCards > RemainingCount * 2
    end.

 % 估算其他玩家的牌数
 estimate_other_players_cards() ->
    15. % 这是一个简化的估算，实际实现需要根据游戏进程动态计算

 % 检查是否有获胜机会
 has_winning_chance(Cards, _LastPlay) ->
    % 简化版本的获胜概率计算
    length(Cards) =< 4.

 % 获取最大的可出牌组合
 get_biggest_play(Cards, LastPlay) ->
    % 实现查找最大牌型的逻辑
    % 这里需要调用 card_rules 模块来判断牌型
    case LastPlay of
        [] -> 
            find_biggest_combination(Cards);
        _ ->
            find_bigger_combination(Cards, LastPlay)
    end.

 % 获取最优的出牌组合
 get_optimal_play(Cards, LastPlay) ->
    % 实现最优出牌策略
    % 考虑因素：手牌数量、牌型组合、出牌节奏等
    case LastPlay of
        [] ->
            find_optimal_combination(Cards);
        _ ->
            find_minimum_bigger_combination(Cards, LastPlay)
    end.
--- a/src/ai_strategy.erl
+++ b/src/ai_strategy.erl
@ -0,0 +1,160 @@
 -module(ai_strategy).
 -export([initialize_strategy/0, update_strategy/2, make_decision/2,
         analyze_game_state/1, evaluate_play/3]).

 -record(game_state, {
    hand_cards,          % 手牌
    played_cards = [],   % 已出牌
    player_position,     % 玩家位置（地主/农民）
    remaining_cards,     % 剩余牌数
    stage,              % 游戏阶段
    last_play,          % 上家出牌
    control_status      % 是否控制局面
 }).

 %% 策略权重配置
 -define(STRATEGY_WEIGHTS, #{
    early_game => #{
        control_weight => 0.7,
        combo_weight => 0.5,
        defensive_weight => 0.3,
        risk_weight => 0.4
    },
    mid_game => #{
        control_weight => 0.5,
        combo_weight => 0.6,
        defensive_weight => 0.5,
        risk_weight => 0.5
    },
    late_game => #{
        control_weight => 0.3,
        combo_weight => 0.8,
        defensive_weight => 0.7,
        risk_weight => 0.6
    }
 }).

 %% 初始化策略系统
 initialize_strategy() ->
    #{
        weights => ?STRATEGY_WEIGHTS,
        learning_rate => 0.01,
        adaptation_rate => 0.1,
        experience => #{},
        history => []
    }.

 %% 更新策略
 update_strategy(Strategy, GameState) ->
    Stage = determine_game_stage(GameState),
    UpdatedWeights = adjust_weights(Strategy, Stage, GameState),
    NewExperience = update_experience(Strategy, GameState),
    Strategy#{
        weights => UpdatedWeights,
        experience => NewExperience,
        history => [GameState | maps:get(history, Strategy)]
    }.

 %% 做出决策
 make_decision(Strategy, GameState) ->
    PossiblePlays = generate_possible_plays(GameState#game_state.hand_cards),
    EvaluatedPlays = evaluate_all_plays(PossiblePlays, Strategy, GameState),
    select_best_play(EvaluatedPlays, Strategy, GameState).

 %% 评估所有可能的出牌
 evaluate_all_plays(Plays, Strategy, GameState) ->
    lists:map(
        fun(Play) ->
            Score = evaluate_play(Play, Strategy, GameState),
            {Play, Score}
        end,
        Plays
    ).

 %% 评估单个出牌
 evaluate_play(Play, Strategy, GameState) ->
    Weights = get_stage_weights(Strategy, GameState#game_state.stage),
    
    ControlScore = evaluate_control(Play, GameState) * maps:get(control_weight, Weights),
    ComboScore = evaluate_combo(Play, GameState) * maps:get(combo_weight, Weights),
    DefensiveScore = evaluate_defensive(Play, GameState) * maps:get(defensive_weight, Weights),
    RiskScore = evaluate_risk(Play, GameState) * maps:get(risk_weight, Weights),
    
    ControlScore + ComboScore + DefensiveScore + RiskScore.

 %% 选择最佳出牌
 select_best_play(EvaluatedPlays, Strategy, GameState) ->
    case should_apply_randomness(Strategy, GameState) of
        true ->
            apply_randomness(EvaluatedPlays);
        false ->
            {Play, _Score} = lists:max(EvaluatedPlays),
            Play
    end.

 %% 内部辅助函数

 determine_game_stage(#game_state{remaining_cards = Remaining}) ->
    case Remaining of
        N when N > 15 -> early_game;
        N when N > 8 -> mid_game;
        _ -> late_game
    end.

 adjust_weights(Strategy, Stage, GameState) ->
    CurrentWeights = maps:get(Stage, maps:get(weights, Strategy)),
    AdaptationRate = maps:get(adaptation_rate, Strategy),
    
    % 基于游戏状态调整权重
    adjust_weight_based_on_state(CurrentWeights, GameState, AdaptationRate).

 update_experience(Strategy, GameState) ->
    Experience = maps:get(experience, Strategy),
    GamePattern = extract_game_pattern(GameState),
    
    maps:update_with(
        GamePattern,
        fun(Count) -> Count + 1 end,
        1,
        Experience
    ).

 evaluate_control(Play, GameState) ->
    case Play of
        pass -> 0.0;
        _ ->
            RemainingControl = calculate_remaining_control(GameState),
            PlayStrength = game_logic:calculate_card_value(Play),
            RemainingControl * PlayStrength / 100.0
    end.

 evaluate_combo(Play, GameState) ->
    RemainingCombos = count_remaining_combos(GameState#game_state.hand_cards -- Play),
    case RemainingCombos of
        0 -> 1.0;
        _ -> 0.8 * (1 - 1/RemainingCombos)
    end.

 evaluate_defensive(Play, GameState) ->
    case GameState#game_state.player_position of
        farmer ->
            evaluate_farmer_defensive(Play, GameState);
        landlord ->
            evaluate_landlord_defensive(Play, GameState)
    end.

 evaluate_risk(Play, GameState) ->
    case is_risky_play(Play, GameState) of
        true -> 0.3;
        false -> 1.0
    end.

 should_apply_randomness(Strategy, GameState) ->
    ExperienceCount = maps:size(maps:get(experience, Strategy)),
    ExperienceCount < 1000 orelse is_close_game(GameState).

 apply_randomness(EvaluatedPlays) ->
    RandomFactor = 0.1,
    Plays = [{Play, Score + (rand:uniform() * RandomFactor)} || {Play, Score} <- EvaluatedPlays],
    {SelectedPlay, _} = lists:max(Plays),
    SelectedPlay.
--- a/src/ai_test.erl
+++ b/src/ai_test.erl
@ -0,0 +1,58 @@
 -module(ai_test).
 -export([run_test/0]).

 run_test() ->
    % 启动所有必要的服务
    {ok, DL} = deep_learning:start_link(),
    {ok, PC} = parallel_compute:start_link(),
    {ok, PM} = performance_monitor:start_link(),
    {ok, VS} = visualization:start_link(),

    % 创建测试数据
    TestData = create_test_data(),
    
    % 训练网络
    {ok, Network} = deep_learning:train_network(test_network, TestData),
    
    % 进行预测
    TestInput = prepare_test_input(),
    {ok, Prediction} = deep_learning:predict(test_network, TestInput),
    
    % 监控性能
    {ok, MonitorId} = performance_monitor:start_monitoring(test_network),
    
    % 等待一些时间收集数据
    timer:sleep(5000),
    
    % 获取性能数据
    {ok, Metrics} = performance_monitor:get_metrics(MonitorId),
    
    % 创建可视化
    {ok, ChartId} = visualization:create_chart(line_chart, Metrics),
    
    % 导出结果
    {ok, Report} = performance_monitor:generate_report(MonitorId),
    {ok, Chart} = visualization:export_chart(ChartId, png),
    
    % 清理资源
    ok = performance_monitor:stop_monitoring(MonitorId),
    
    % 返回测试结果
    #{
        prediction => Prediction,
        metrics => Metrics,
        report => Report,
        chart => Chart
    }.

 % 辅助函数
 create_test_data() ->
    [
        {[1,2,3], [4]},
        {[2,3,4], [5]},
        {[3,4,5], [6]},
        {[4,5,6], [7]}
    ].

 prepare_test_input() ->
    [5,6,7].
--- a/src/auto_player.erl
+++ b/src/auto_player.erl
@ -0,0 +1,106 @@
 -module(auto_player).
 -behaviour(gen_server).

 -export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([take_over/1, release/1, is_auto_playing/1]).

 -record(state, {
    controlled_players = #{}, % Map: PlayerPid -> OriginalPlayer
    active_games = #{}       % Map: GamePid -> [{PlayerPid, Cards}]
 }).

 %% API
 start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

 take_over(PlayerPid) ->
    gen_server:call(?MODULE, {take_over, PlayerPid}).

 release(PlayerPid) ->
    gen_server:call(?MODULE, {release, PlayerPid}).

 is_auto_playing(PlayerPid) ->
    gen_server:call(?MODULE, {is_auto_playing, PlayerPid}).

 %% Callbacks
 init([]) ->
    {ok, #state{}}.

 handle_call({take_over, PlayerPid}, _From, State = #state{controlled_players = Players}) ->
    case maps:is_key(PlayerPid, Players) of
        true ->
            {reply, {error, already_controlled}, State};
        false ->
            NewPlayers = maps:put(PlayerPid, {auto, os:timestamp()}, Players),
            {reply, ok, State#state{controlled_players = NewPlayers}}
    end;

 handle_call({release, PlayerPid}, _From, State = #state{controlled_players = Players}) ->
    NewPlayers = maps:remove(PlayerPid, Players),
    {reply, ok, State#state{controlled_players = NewPlayers}};

 handle_call({is_auto_playing, PlayerPid}, _From, State = #state{controlled_players = Players}) ->
    {reply, maps:is_key(PlayerPid, Players), State};

 handle_call(_Request, _From, State) ->
    {reply, {error, unknown_call}, State}.

 handle_cast({game_update, GamePid, PlayerPid, Cards}, State) ->
    NewState = update_game_state(GamePid, PlayerPid, Cards, State),
    maybe_play_turn(GamePid, PlayerPid, NewState),
    {noreply, NewState};

 handle_cast(_Msg, State) ->
    {noreply, State}.

 handle_info(_Info, State) ->
    {noreply, State}.

 %% 内部函数

 update_game_state(GamePid, PlayerPid, Cards, State = #state{active_games = Games}) ->
    GamePlayers = maps:get(GamePid, Games, []),
    UpdatedPlayers = lists:keystore(PlayerPid, 1, GamePlayers, {PlayerPid, Cards}),
    NewGames = maps:put(GamePid, UpdatedPlayers, Games),
    State#state{active_games = NewGames}.

 maybe_play_turn(GamePid, PlayerPid, State) ->
    case should_play_turn(GamePid, PlayerPid, State) of
        true ->
            timer:sleep(rand:uniform(1000) + 500), % 添加随机延迟使行为更自然
            Play = calculate_auto_play(GamePid, PlayerPid, State),
            execute_play(GamePid, PlayerPid, Play);
        false ->
            ok
    end.

 should_play_turn(GamePid, PlayerPid, #state{controlled_players = Players}) ->
    maps:is_key(PlayerPid, Players) andalso is_current_player(GamePid, PlayerPid).

 is_current_player(GamePid, PlayerPid) ->
    % 从游戏服务器获取当前玩家
    case game_server:get_current_player(GamePid) of
        {ok, CurrentPlayer} -> CurrentPlayer =:= PlayerPid;
        _ -> false
    end.

 calculate_auto_play(GamePid, PlayerPid, #state{active_games = Games}) ->
    GameState = maps:get(GamePid, Games, []),
    {_, Cards} = lists:keyfind(PlayerPid, 1, GameState),
    LastPlay = game_server:get_last_play(GamePid),
    case can_beat_play(Cards, LastPlay) of
        {true, Play} -> {play, Play};
        false -> pass
    end.

 execute_play(_GamePid, _PlayerPid, pass) ->
    game_server:pass(_GamePid, _PlayerPid);
 execute_play(_GamePid, _PlayerPid, {play, Cards}) ->
    game_server:play_cards(_GamePid, _PlayerPid, Cards).

 can_beat_play(Cards, LastPlay) ->
    % 使用 card_rules 模块来找出可以压过上家的牌
    case card_rules:find_valid_plays(Cards, LastPlay) of
        [] -> false;
        [BestPlay|_] -> {true, BestPlay}
    end.
--- a/src/cardSrv.app.src
+++ b/src/cardSrv.app.src
@ -0,0 +1,15 @@
 {application, cardSrv,
 [{description, "An OTP application"},
  {vsn, "0.1.0"},
  {registered, []},
  {mod, {cardSrv_app, []}},
  {applications,
   [kernel,
    stdlib
   ]},
  {env,[]},
  {modules, []},

  {licenses, ["Apache-2.0"]},
  {links, []}
 ]}.
--- a/src/cardSrv_app.erl
+++ b/src/cardSrv_app.erl
@ -0,0 +1,18 @@
 %%%-------------------------------------------------------------------
 %% @doc cardSrv public API
 %% @end
 %%%-------------------------------------------------------------------

 -module(cardSrv_app).

 -behaviour(application).

 -export([start/2, stop/1]).

 start(_StartType, _StartArgs) ->
    cardSrv_sup:start_link().

 stop(_State) ->
    ok.

 %% internal functions
--- a/src/cardSrv_sup.erl
+++ b/src/cardSrv_sup.erl
@ -0,0 +1,35 @@
 %%%-------------------------------------------------------------------
 %% @doc cardSrv top level supervisor.
 %% @end
 %%%-------------------------------------------------------------------

 -module(cardSrv_sup).

 -behaviour(supervisor).

 -export([start_link/0]).

 -export([init/1]).

 -define(SERVER, ?MODULE).

 start_link() ->
    supervisor:start_link({local, ?SERVER}, ?MODULE, []).

 %% sup_flags() = #{strategy => strategy(),         % optional
 %%                 intensity => non_neg_integer(), % optional
 %%                 period => pos_integer()}        % optional
 %% child_spec() = #{id => child_id(),       % mandatory
 %%                  start => mfargs(),      % mandatory
 %%                  restart => restart(),   % optional
 %%                  shutdown => shutdown(), % optional
 %%                  type => worker(),       % optional
 %%                  modules => modules()}   % optional
 init([]) ->
    SupFlags = #{strategy => one_for_all,
                 intensity => 0,
                 period => 1},
    ChildSpecs = [],
    {ok, {SupFlags, ChildSpecs}}.

 %% internal functions
--- a/src/card_rules.erl
+++ b/src/card_rules.erl
@ -0,0 +1,93 @@
 -module(card_rules).
 -export([validate_play/2, get_card_type/1, compare_cards/2]).

 %% 验证出牌是否合法
 validate_play(Cards, LastPlay) ->
    case {get_card_type(Cards), get_card_type(LastPlay)} of
        {invalid, _} -> false;
        {_, undefined} -> true; % 首次出牌
        {Type, Type} -> compare_cards(Cards, LastPlay);
        {bomb, _} -> true;
        {rocket, _} -> true;
        _ -> false
    end.

 %% 获取牌型
 get_card_type(Cards) ->
    case length(Cards) of
        0 -> undefined;
        1 -> single;
        2 -> check_pair(Cards);
        3 -> check_three(Cards);
        4 -> check_bomb_or_three_one(Cards);
        _ -> check_sequence(Cards)
    end.

 %% 检查对子
 check_pair([{_, N}, {_, N}]) -> pair;
 check_pair(_) -> invalid.

 %% 检查三张
 check_three([{_, N}, {_, N}, {_, N}]) -> three;
 check_three(_) -> invalid.

 %% 检查炸弹或三带一
 check_bomb_or_three_one(Cards) ->
    Grouped = group_cards(Cards),
    case Grouped of
        [{_, 4}|_] -> bomb;
        [{_, 3}, {_, 1}] -> three_one;
        _ -> invalid
    end.

 %% 检查顺子等
 check_sequence(Cards) ->
    case is_straight(Cards) of
        true -> straight;
        false -> check_other_types(Cards)
    end.

 %% 比较牌大小
 compare_cards(Cards1, Cards2) ->
    case {get_card_type(Cards1), get_card_type(Cards2)} of
        {rocket, _} -> true;
        {bomb, OtherType} when OtherType =/= bomb -> true;
        {Same, Same} -> compare_value(Cards1, Cards2);
        _ -> false
    end.

 %% 辅助函数 - 对牌进行分组
 group_cards(Cards) ->
    Dict = lists:foldl(
        fun({_, N}, Acc) ->
            dict:update_counter(N, 1, Acc)
        end,
        dict:new(),
        Cards
    ),
    lists:sort(
        fun({_, Count1}, {_, Count2}) -> Count1 >= Count2 end,
        dict:to_list(Dict)
    ).

 %% 辅助函数 - 判断是否为顺子
 is_straight(Cards) ->
    CardValues = [{"3",3}, {"4",4}, {"5",5}, {"6",6}, {"7",7}, {"8",8}, {"9",9}, 
                 {"10",10}, {"J",11}, {"Q",12}, {"K",13}, {"A",14}, {"2",15}],
    Values = [proplists:get_value(N, CardValues) || {_, N} <- Cards],
    SortedValues = lists:sort(Values),
    case length(SortedValues) >= 5 of
        true ->
            lists:all(fun({A, B}) -> B - A =:= 1 end, 
                     lists:zip(SortedValues, tl(SortedValues)));
        false -> false
    end.

 %% 比较相同牌型的大小
 compare_value(Cards1, Cards2) ->
    CardValues = [{"3",3}, {"4",4}, {"5",5}, {"6",6}, {"7",7}, {"8",8}, {"9",9}, 
                 {"10",10}, {"J",11}, {"Q",12}, {"K",13}, {"A",14}, {"2",15},
                 {"小王",16}, {"大王",17}],
    Max1 = lists:max([proplists:get_value(N, CardValues) || {_, N} <- Cards1]),
    Max2 = lists:max([proplists:get_value(N, CardValues) || {_, N} <- Cards2]),
    Max1 > Max2.
--- a/src/cards.erl
+++ b/src/cards.erl
@ -0,0 +1,34 @@
 -module(cards).
 -export([init_cards/0, shuffle_cards/1, deal_cards/1, sort_cards/1]).

 %% 初始化一副牌
 init_cards() ->
    Colors = ["♠", "♥", "♣", "♦"],
    Numbers = ["3","4","5","6","7","8","9","10","J","Q","K","A","2"],
    Cards = [{Color, Number} || Color <- Colors, Number <- Numbers],
    [{joker, "小王"}, {joker, "大王"}] ++ Cards.

 %% 洗牌
 shuffle_cards(Cards) ->
    List = [{rand:uniform(), Card} || Card <- Cards],
    [Card || {_, Card} <- lists:sort(List)].

 %% 发牌 - 返回{Player1Cards, Player2Cards, Player3Cards, LandlordCards}
 deal_cards(Cards) ->
    {First17, Rest} = lists:split(17, Cards),
    {Second17, Rest2} = lists:split(17, Rest),
    {Third17, LandlordCards} = lists:split(17, Rest2),
    {First17, Second17, Third17, LandlordCards}.

 %% 排序牌 - 根据大小排序
 sort_cards(Cards) ->
    CardValues = [{"3",3}, {"4",4}, {"5",5}, {"6",6}, {"7",7}, {"8",8}, {"9",9}, 
                 {"10",10}, {"J",11}, {"Q",12}, {"K",13}, {"A",14}, {"2",15},
                 {"小王",16}, {"大王",17}],
    lists:sort(
        fun({_, N1}, {_, N2}) ->
            {_, V1} = lists:keyfind(N1, 1, CardValues),
            {_, V2} = lists:keyfind(N2, 1, CardValues),
            V1 =< V2
        end,
        Cards).
--- a/src/decision_engine.erl
+++ b/src/decision_engine.erl
@ -0,0 +1,53 @@
 -module(decision_engine).
 -export([make_decision/3, evaluate_options/2, calculate_win_probability/2]).

 make_decision(GameState, AIState, Options) ->
    % 深度评估每个选项
    EvaluatedOptions = deep_evaluate_options(Options, GameState, AIState),
    
    % 计算胜率
    WinProbabilities = calculate_win_probabilities(EvaluatedOptions, GameState),
    
    % 风险评估
    RiskAnalysis = analyze_risks(EvaluatedOptions, GameState),
    
    % 综合决策
    BestOption = select_optimal_option(EvaluatedOptions, WinProbabilities, RiskAnalysis),
    
    % 应用最终决策
    apply_decision(BestOption, GameState, AIState).

 deep_evaluate_options(Options, GameState, AIState) ->
    lists:map(
        fun(Option) ->
            % 深度搜索评估
            SearchResult = monte_carlo_search(Option, GameState, 1000),
            
            % 策略评估
            StrategyScore = strategy_optimizer:evaluate_strategy(Option, GameState),
            
            % 对手反应预测
            OpponentReaction = opponent_modeling:predict_play(AIState#ai_state.opponent_model, GameState),
            
            % 综合评分
            {Option, calculate_comprehensive_score(SearchResult, StrategyScore, OpponentReaction)}
        end,
        Options
    ).

 calculate_win_probability(Option, GameState) ->
    % 基于当前局势分析
    SituationScore = analyze_situation_score(GameState),
    
    % 基于牌型分析
    PatternScore = analyze_pattern_strength(Option),
    
    % 基于对手模型
    OpponentScore = analyze_opponent_factors(GameState),
    
    % 计算综合胜率
    calculate_combined_probability([
        {SituationScore, 0.4},
        {PatternScore, 0.3},
        {OpponentScore, 0.3}
    ]).
--- a/src/deep_learning.erl
+++ b/src/deep_learning.erl
@ -0,0 +1,104 @@
 -module(deep_learning).
 -behaviour(gen_server).

 -export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([train_network/2, predict/2, update_network/2, get_network_stats/1]).

 -record(state, {
    networks = #{},        % Map: NetworkName -> NetworkData
    training_queue = [],   % 训练队列
    batch_size = 32,      % 批次大小
    learning_rate = 0.001  % 学习率
 }).

 -record(network, {
    layers = [],          % 网络层结构
    weights = #{},        % 权重
    biases = #{},         % 偏置
    activation = relu,    % 激活函数
    optimizer = adam,     % 优化器
    loss_history = [],    % 损失历史
    accuracy_history = [] % 准确率历史
 }).

 %% API
 start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

 train_network(NetworkName, TrainingData) ->
    gen_server:call(?MODULE, {train, NetworkName, TrainingData}).

 predict(NetworkName, Input) ->
    gen_server:call(?MODULE, {predict, NetworkName, Input}).

 update_network(NetworkName, Gradients) ->
    gen_server:cast(?MODULE, {update, NetworkName, Gradients}).

 get_network_stats(NetworkName) ->
    gen_server:call(?MODULE, {get_stats, NetworkName}).

 %% 内部函数

 initialize_network(LayerSizes) ->
    Layers = create_layers(LayerSizes),
    Weights = initialize_weights(Layers),
    Biases = initialize_biases(Layers),
    #network{
        layers = Layers,
        weights = Weights,
        biases = Biases
    }.

 create_layers(Sizes) ->
    lists:map(fun(Size) ->
        #{size => Size, type => dense}
    end, Sizes).

 initialize_weights(Layers) ->
    lists:foldl(
        fun(Layer, Acc) ->
            Size = maps:get(size, Layer),
            W = random_matrix(Size, Size),
            maps:put(Size, W, Acc)
        end,
        #{},
        Layers
    ).

 random_matrix(Rows, Cols) ->
    matrix:new(Rows, Cols, fun() -> rand:uniform() - 0.5 end).

 forward_propagation(Input, Network) ->
    #network{layers = Layers, weights = Weights, biases = Biases} = Network,
    lists:foldl(
        fun(Layer, Acc) ->
            W = maps:get(maps:get(size, Layer), Weights),
            B = maps:get(maps:get(size, Layer), Biases),
            Z = matrix:add(matrix:multiply(W, Acc), B),
            activate(Z, Network#network.activation)
        end,
        Input,
        Layers
    ).

 backward_propagation(Network, Input, Target) ->
    % 实现反向传播算法
    {Gradients, Loss} = calculate_gradients(Network, Input, Target),
    {Network#network{
        weights = update_weights(Network#network.weights, Gradients),
        loss_history = [Loss | Network#network.loss_history]
    }, Loss}.

 activate(Z, relu) ->
    matrix:map(fun(X) -> max(0, X) end, Z);
 activate(Z, sigmoid) ->
    matrix:map(fun(X) -> 1 / (1 + math:exp(-X)) end, Z);
 activate(Z, tanh) ->
    matrix:map(fun(X) -> math:tanh(X) end, Z).

 optimize(Network, Gradients, adam) ->
    % 实现Adam优化器
    update_adam(Network, Gradients);
 optimize(Network, Gradients, sgd) ->
    % 实现随机梯度下降
    update_sgd(Network, Gradients).
--- a/src/game_core.erl
+++ b/src/game_core.erl
@ -0,0 +1,136 @@
 -module(game_core).
 -export([start_game/3, play_cards/3, get_game_state/1, is_valid_play/2]).
 -export([init_deck/0, deal_cards/1, get_card_type/1, compare_cards/2]).

 -record(game_state, {
    players = [],          % [{Pid, Cards, Role}]
    current_player,        % Pid
    last_play = [],        % {Pid, Cards}
    played_cards = [],     % [{Pid, Cards}]
    stage = waiting,       % waiting | playing | finished
    landlord_cards = []    % 地主牌
 }).

 %% 初始化扑克牌
 init_deck() ->
    Colors = ["♠", "♥", "♣", "♦"],
    Numbers = ["3", "4", "5", "6", "7", "8", "9", "10", "J", "Q", "K", "A", "2"],
    Cards = [{Color, Number} || Color <- Colors, Number <- Numbers],
    Jokers = [{"", "小王"}, {"", "大王"}],
    shuffle(Cards ++ Jokers).

 %% 发牌
 deal_cards(Deck) ->
    {Players, Landlord} = lists:split(51, Deck),
    {lists:sublist(Players, 1, 17),
     lists:sublist(Players, 18, 17),
     lists:sublist(Players, 35, 17),
     Landlord}.

 %% 开始游戏
 start_game(Player1, Player2, Player3) ->
    Deck = init_deck(),
    {Cards1, Cards2, Cards3, LandlordCards} = deal_cards(Deck),
    % 随机选择地主
    LandlordIdx = rand:uniform(3),
    Players = assign_roles([{Player1, Cards1}, {Player2, Cards2}, {Player3, Cards3}], LandlordIdx),
    #game_state{
        players = Players,
        current_player = element(1, lists:nth(LandlordIdx, Players)),
        landlord_cards = LandlordCards
    }.

 %% 出牌
 play_cards(GameState, PlayerPid, Cards) ->
    case validate_play(GameState, PlayerPid, Cards) of
        true ->
            NewState = update_game_state(GameState, PlayerPid, Cards),
            check_game_end(NewState);
        false ->
            {error, invalid_play}
    end.

 %% 验证出牌
 validate_play(GameState, PlayerPid, Cards) ->
    case get_player_cards(GameState, PlayerPid) of
        {ok, PlayerCards} ->
            has_cards(PlayerCards, Cards) andalso
            is_valid_play(Cards, GameState#game_state.last_play);
        _ ->
            false
    end.

 %% 判断牌型
 get_card_type(Cards) ->
    Sorted = sort_cards(Cards),
    case analyze_pattern(Sorted) of
        {Type, Value} -> {ok, Type, Value};
        invalid -> {error, invalid_pattern}
    end.

 %% 内部函数

 shuffle(List) ->
    [X || {_, X} <- lists:sort([{rand:uniform(), N} || N <- List])].

 analyze_pattern(Cards) ->
    case length(Cards) of
        1 -> {single, card_value(hd(Cards))};
        2 -> analyze_pair(Cards);
        3 -> analyze_triple(Cards);
        4 -> analyze_four_cards(Cards);
        _ -> analyze_complex_pattern(Cards)
    end.

 analyze_pair([{_, N}, {_, N}]) -> {pair, card_value({any, N})};
 analyze_pair(_) -> invalid.

 analyze_triple([{_, N}, {_, N}, {_, N}]) -> {triple, card_value({any, N})};
 analyze_triple(_) -> invalid.

 analyze_four_cards(Cards) ->
    case group_cards(Cards) of
        #{4 := [Value]} -> {bomb, card_value({any, Value})};
        _ -> analyze_four_with_two(Cards)
    end.

 analyze_complex_pattern(Cards) ->
    case is_straight(Cards) of
        true -> {straight, highest_card_value(Cards)};
        false -> analyze_other_patterns(Cards)
    end.

 %% 辅助函数

 card_value({_, "A"}) -> 14;
 card_value({_, "2"}) -> 15;
 card_value({_, "小王"}) -> 16;
 card_value({_, "大王"}) -> 17;
 card_value({_, N}) when is_list(N) -> 
    try list_to_integer(N)
    catch _:_ -> 
        case N of
            "J" -> 11;
            "Q" -> 12;
            "K" -> 13
        end
    end.

 sort_cards(Cards) ->
    lists:sort(fun(A, B) -> card_value(A) =< card_value(B) end, Cards).

 group_cards(Cards) ->
    lists:foldl(
        fun({_, N}, Acc) ->
            maps:update_with(N, fun(L) -> [N|L] end, [N], Acc)
        end,
        #{},
        Cards
    ).

 is_straight(Cards) ->
    Values = [card_value(C) || C <- Cards],
    Sorted = lists:sort(Values),
    length(Sorted) >= 5 andalso
    lists:all(fun({A, B}) -> B - A =:= 1 end, 
             lists:zip(Sorted, tl(Sorted))).
--- a/src/game_logic.erl
+++ b/src/game_logic.erl
@ -0,0 +1,171 @@
 -module(game_logic).
 -export([validate_play/2, calculate_card_value/1, evaluate_hand_strength/1,
         find_all_possible_patterns/1, analyze_card_pattern/1]).

 -define(CARD_VALUES, #{
    "3" => 3, "4" => 4, "5" => 5, "6" => 6, "7" => 7, "8" => 8, "9" => 9,
    "10" => 10, "J" => 11, "Q" => 12, "K" => 13, "A" => 14, "2" => 15,
    "小王" => 16, "大王" => 17
 }).

 %% 牌型定义
 -record(card_pattern, {
    type,        % single | pair | triple | triple_with_one | straight | 
                 % straight_pair | airplane | airplane_with_wings | four_with_two | bomb | rocket
    value,       % 主牌值
    length = 1,  % 顺子长度
    extra = []   % 附加牌
 }).

 %% 验证出牌是否合法
 validate_play(Cards, LastPlay) ->
    case analyze_card_pattern(Cards) of
        {invalid, _} -> 
            false;
        {Pattern, Value} -> 
            case analyze_card_pattern(LastPlay) of
                {Pattern, LastValue} when Value > LastValue -> 
                    true;
                {_, _} -> 
                    check_special_cases(Pattern, Value, LastPlay);
                _ -> 
                    false
            end
    end.

 %% 分析牌型
 analyze_card_pattern(Cards) ->
    Grouped = group_cards(Cards),
    case identify_pattern(Grouped) of
        {Type, Value} when Type =/= invalid ->
            {#card_pattern{type = Type, value = Value}, Value};
        _ ->
            {invalid, 0}
    end.

 %% 计算手牌价值
 calculate_card_value(Cards) ->
    {Pattern, BaseValue} = analyze_card_pattern(Cards),
    case Pattern#card_pattern.type of
        bomb -> BaseValue * 2;
        rocket -> 1000;
        _ -> BaseValue
    end.

 %% 评估手牌强度
 evaluate_hand_strength(Cards) ->
    Patterns = find_all_possible_patterns(Cards),
    BaseScore = lists:foldl(
        fun(Pattern, Acc) ->
            Acc + calculate_pattern_value(Pattern)
        end,
        0,
        Patterns
    ),
    adjust_score_by_combination(BaseScore, Cards).

 %% 找出所有可能的牌型组合
 find_all_possible_patterns(Cards) ->
    Grouped = group_cards(Cards),
    Singles = find_singles(Grouped),
    Pairs = find_pairs(Grouped),
    Triples = find_triples(Grouped),
    Straights = find_straights(Cards),
    StraightPairs = find_straight_pairs(Grouped),
    Airplanes = find_airplanes(Grouped),
    Bombs = find_bombs(Grouped),
    Rockets = find_rockets(Cards),
    Singles ++ Pairs ++ Triples ++ Straights ++ StraightPairs ++ 
    Airplanes ++ Bombs ++ Rockets.

 %% 内部辅助函数

 group_cards(Cards) ->
    lists:foldl(
        fun({_, Number}, Acc) ->
            maps:update_with(
                Number,
                fun(Count) -> Count + 1 end,
                1,
                Acc
            )
        end,
        #{},
        Cards
    ).

 identify_pattern(Grouped) ->
    case maps:size(Grouped) of
        1 -> 
            [{Number, Count}] = maps:to_list(Grouped),
            case Count of
                1 -> {single, maps:get(Number, ?CARD_VALUES)};
                2 -> {pair, maps:get(Number, ?CARD_VALUES)};
                3 -> {triple, maps:get(Number, ?CARD_VALUES)};
                4 -> {bomb, maps:get(Number, ?CARD_VALUES)};
                _ -> {invalid, 0}
            end;
        _ ->
            identify_complex_pattern(Grouped)
    end.

 identify_complex_pattern(Grouped) ->
    case find_straight_pattern(Grouped) of
        {ok, Pattern} -> Pattern;
        false ->
            case find_airplane_pattern(Grouped) of
                {ok, Pattern} -> Pattern;
                false ->
                    case find_four_with_two(Grouped) of
                        {ok, Pattern} -> Pattern;
                        false -> {invalid, 0}
                    end
            end
    end.

 find_straight_pattern(Grouped) ->
    Numbers = lists:sort(maps:keys(Grouped)),
    case is_consecutive(Numbers) of
        true when length(Numbers) >= 5 ->
            MaxValue = lists:max([maps:get(N, ?CARD_VALUES) || N <- Numbers]),
            {ok, {straight, MaxValue}};
        _ ->
            false
    end.

 is_consecutive(Numbers) ->
    case Numbers of
        [] -> true;
        [_] -> true;
        [First|Rest] ->
            lists:all(
                fun({A, B}) -> maps:get(B, ?CARD_VALUES) - maps:get(A, ?CARD_VALUES) =:= 1 end,
                lists:zip(Numbers, Rest)
            )
    end.

 calculate_pattern_value(Pattern) ->
    case Pattern of
        #card_pattern{type = single, value = V} -> V;
        #card_pattern{type = pair, value = V} -> V * 2;
        #card_pattern{type = triple, value = V} -> V * 3;
        #card_pattern{type = bomb, value = V} -> V * 4;
        #card_pattern{type = rocket} -> 1000;
        #card_pattern{type = straight, value = V, length = L} -> V * L;
        _ -> 0
    end.

 adjust_score_by_combination(BaseScore, Cards) ->
    CombinationBonus = case length(Cards) of
        N when N =< 5 -> BaseScore * 1.2;
        N when N =< 10 -> BaseScore * 1.1;
        _ -> BaseScore
    end,
    round(CombinationBonus).

 check_special_cases(Pattern, Value, LastPlay) ->
    case Pattern#card_pattern.type of
        bomb -> true;
        rocket -> true;
        _ -> false
    end.
--- a/src/game_manager.erl
+++ b/src/game_manager.erl
@ -0,0 +1,51 @@
 -module(game_manager).
 -export([start_game/3, handle_play/2, end_game/1]).

 -record(game_manager_state, {
    game_id,
    players,
    ai_players,
    current_state,
    history
 }).

 start_game(Player1, Player2, Player3) ->
    % 初始化游戏状态
    GameState = game_core:init_game_state(),
    
    % 初始化AI玩家
    AIPlayers = initialize_ai_players(),
    
    % 创建游戏管理器状态
    GameManagerState = #game_manager_state{
        game_id = generate_game_id(),
        players = [Player1, Player2, Player3],
        ai_players = AIPlayers,
        current_state = GameState,
        history = []
    },
    
    % 开始游戏循环
    game_loop(GameManagerState).

 handle_play(GameManagerState, Play) ->
    % 验证玩家行动
    case game_core:validate_play(GameManagerState#game_manager_state.current_state, Play) of
        true ->
            % 更新游戏状态
            NewState = game_core:update_state(GameManagerState#game_manager_state.current_state, Play),
            
            % 更新AI玩家
            UpdatedAIPlayers = update_ai_players(GameManagerState#game_manager_state.ai_players, Play),
            
            % 记录历史
            NewHistory = [Play | GameManagerState#game_manager_state.history],
            
            GameManagerState#game_manager_state{
                current_state = NewState,
                ai_players = UpdatedAIPlayers,
                history = NewHistory
            };
        false ->
            {error, invalid_play}
    end.
--- a/src/game_server.erl
+++ b/src/game_server.erl
@ -0,0 +1,191 @@
 -module(game_server).
 -behaviour(gen_server).

 -export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([create_game/0, join_game/2, start_game/1, play_cards/3, pass/2]).

 -record(state, {
    players = [],         % [{PlayerPid, Cards}]
    current_player = none,
    last_play = [],
    landlord = none,
    game_status = waiting % waiting | playing | finished
 }).

 %% API
 start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

 create_game() ->
    gen_server:call(?MODULE, create_game).

 join_game(GamePid, PlayerPid) ->
    gen_server:call(GamePid, {join_game, PlayerPid}).

 start_game(GamePid) ->
    gen_server:call(GamePid, start_game).

 play_cards(GamePid, PlayerPid, Cards) ->
    gen_server:call(GamePid, {play_cards, PlayerPid, Cards}).

 pass(GamePid, PlayerPid) ->
    gen_server:call(GamePid, {pass, PlayerPid}).

 %% Callbacks
 init([]) ->
    {ok, #state{}}.

 handle_call(create_game, _From, State) ->
    {reply, {ok, self()}, State#state{players = [], game_status = waiting}};

 handle_call({join_game, PlayerPid}, _From, State = #state{players = Players}) ->
    case length(Players) < 3 of
        true ->
            NewPlayers = [{PlayerPid, []} | Players],
            {reply, {ok, length(NewPlayers)}, State#state{players = NewPlayers}};
        false ->
            {reply, {error, game_full}, State}
    end;

 handle_call(start_game, _From, State = #state{players = Players}) ->
    case length(Players) =:= 3 of
        true ->
            % 初始化并洗牌
            Cards = cards:shuffle_cards(cards:init_cards()),
            {P1Cards, P2Cards, P3Cards, LandlordCards} = cards:deal_cards(Cards),
            % 随机选择地主
            LandlordIndex = rand:uniform(3),
            {NewPlayers, NewLandlord} = assign_cards_and_landlord(Players, [P1Cards, P2Cards, P3Cards], LandlordCards, LandlordIndex),
            {reply, {ok, NewLandlord}, State#state{
                players = NewPlayers,
                current_player = NewLandlord,
                game_status = playing,
                landlord = NewLandlord
            }};
        false ->
            {reply, {error, not_enough_players}, State}
    end;

 handle_call({play_cards, PlayerPid, Cards}, _From, State = #state{current_player = PlayerPid, last_play = LastPlay, players = Players}) ->
    case card_rules:validate_play(Cards, LastPlay) of
        true ->
            case remove_cards(PlayerPid, Cards, Players) of
                {ok, NewPlayers} ->
                    NextPlayer = get_next_player(PlayerPid, Players),
                    case check_winner(NewPlayers) of
                        {winner, Winner} ->
                            {reply, {ok, winner, Winner}, State#state{
                                players = NewPlayers,
                                game_status = finished
                            }};
                        no_winner ->
                            {reply, {ok, next_player, NextPlayer}, State#state{
                                players = NewPlayers,
                                current_player = NextPlayer,
                                last_play = Cards
                            }}
                    end;
                error ->
                    {reply, {error, invalid_cards}, State}
            end;
        false ->
            {reply, {error, invalid_play}, State}
    end;

 handle_call({pass, PlayerPid}, _From, State = #state{current_player = PlayerPid, last_play = LastPlay, players = Players}) ->
    case LastPlay of
        [] ->
            {reply, {error, cannot_pass}, State};
        _ ->
            NextPlayer = get_next_player(PlayerPid, Players),
            {reply, {ok, next_player, NextPlayer}, State#state{current_player = NextPlayer}}
    end;

 handle_call(_, _From, State) ->
    {reply, {error, invalid_call}, State}.

 handle_cast(_, State) ->
    {noreply, State}.

 handle_info(_, State) ->
    {noreply, State}.

 terminate(_Reason, _State) ->
    ok.

 code_change(_OldVsn, State, _Extra) ->
    {ok, State}.

 %% 内部辅助函数

 %% 分配牌和地主
 assign_cards_and_landlord(Players, [P1, P2, P3], LandlordCards, LandlordIndex) ->
    {Pid1, _} = lists:nth(1, Players),
    {Pid2, _} = lists:nth(2, Players),
    {Pid3, _} = lists:nth(3, Players),
    
    case LandlordIndex of
        1 ->
            {[{Pid1, P1 ++ LandlordCards}, {Pid2, P2}, {Pid3, P3}], Pid1};
        2 ->
            {[{Pid1, P1}, {Pid2, P2 ++ LandlordCards}, {Pid3, P3}], Pid2};
        3 ->
            {[{Pid1, P1}, {Pid2, P2}, {Pid3, P3 ++ LandlordCards}], Pid3}
    end.

 %% 获取下一个玩家
 get_next_player(CurrentPid, Players) ->
    PlayerPids = [Pid || {Pid, _} <- Players],
    CurrentIndex = get_player_index(CurrentPid, PlayerPids),
    lists:nth(1 + ((CurrentIndex) rem 3), PlayerPids).

 %% 获取玩家索引
 get_player_index(Pid, Pids) ->
    {Index, _} = lists:foldl(
        fun(P, {Idx, Found}) ->
            case P =:= Pid of
                true -> {Idx, Idx};
                false -> {Idx + 1, Found}
            end
        end,
        {1, none},
        Pids
    ),
    Index.

 %% 移除玩家手中的牌
 remove_cards(PlayerPid, CardsToRemove, Players) ->
    case lists:keyfind(PlayerPid, 1, Players) of
        {PlayerPid, PlayerCards} ->
            case can_remove_cards(CardsToRemove, PlayerCards) of
                true ->
                    NewCards = PlayerCards -- CardsToRemove,
                    NewPlayers = lists:keyreplace(PlayerPid, 1, Players, {PlayerPid, NewCards}),
                    {ok, NewPlayers};
                false ->
                    error
            end;
        false ->
            error
    end.

 %% 检查是否能移除指定的牌
 can_remove_cards(CardsToRemove, PlayerCards) ->
    lists:all(
        fun(Card) ->
            lists:member(Card, PlayerCards)
        end,
        CardsToRemove
    ).

 %% 检查是否有获胜者
 check_winner(Players) ->
    case lists:filter(
        fun({_, Cards}) ->
            length(Cards) =:= 0
        end,
        Players
    ) of
        [{Winner, _}|_] -> {winner, Winner};
        [] -> no_winner
    end.
--- a/src/matrix.erl
+++ b/src/matrix.erl
@ -0,0 +1,112 @@
 -module(matrix).
 -export([new/3, multiply/2, add/2, subtract/2, transpose/1, map/2]).
 -export([from_list/1, to_list/1, get/3, set/4, shape/1]).

 -record(matrix, {
    rows,
    cols,
    data
 }).

 new(Rows, Cols, InitFun) when is_integer(Rows), is_integer(Cols), Rows > 0, Cols > 0 ->
    Data = array:new(Rows * Cols, {default, 0.0}),
    Data2 = case is_function(InitFun) of
        true ->
            lists:foldl(
                fun(I, Acc) ->
                    lists:foldl(
                        fun(J, Acc2) ->
                            array:set(I * Cols + J, InitFun(), Acc2)
                        end,
                        Acc,
                        lists:seq(0, Cols-1)
                    )
                end,
                Data,
                lists:seq(0, Rows-1)
            );
        false ->
            array:set_value(InitFun, Data)
    end,
    #matrix{rows = Rows, cols = Cols, data = Data2}.

 multiply(#matrix{rows = M, cols = N, data = Data1},
        #matrix{rows = N, cols = P, data = Data2}) ->
    Result = array:new(M * P, {default, 0.0}),
    ResultData = lists:foldl(
        fun(I, Acc1) ->
            lists:foldl(
                fun(J, Acc2) ->
                    Sum = lists:sum([
                        array:get(I * N + K, Data1) * array:get(K * P + J, Data2)
                        || K <- lists:seq(0, N-1)
                    ]),
                    array:set(I * P + J, Sum, Acc2)
                end,
                Acc1,
                lists:seq(0, P-1)
            )
        end,
        Result,
        lists:seq(0, M-1)
    ),
    #matrix{rows = M, cols = P, data = ResultData}.

 add(#matrix{rows = R, cols = C, data = Data1},
    #matrix{rows = R, cols = C, data = Data2}) ->
    NewData = array:map(
        fun(I, V) -> V + array:get(I, Data2) end,
        Data1
    ),
    #matrix{rows = R, cols = C, data = NewData}.

 subtract(#matrix{rows = R, cols = C, data = Data1},
        #matrix{rows = R, cols = C, data = Data2}) ->
    NewData = array:map(
        fun(I, V) -> V - array:get(I, Data2) end,
        Data1
    ),
    #matrix{rows = R, cols = C, data = NewData}.

 transpose(#matrix{rows = R, cols = C, data = Data}) ->
    NewData = array:new(R * C, {default, 0.0}),
    TransposedData = lists:foldl(
        fun(I, Acc1) ->
            lists:foldl(
                fun(J, Acc2) ->
                    array:set(J * R + I, array:get(I * C + J, Data), Acc2)
                end,
                Acc1,
                lists:seq(0, C-1)
            )
        end,
        NewData,
        lists:seq(0, R-1)
    ),
    #matrix{rows = C, cols = R, data = TransposedData}.

 map(Fun, #matrix{rows = R, cols = C, data = Data}) ->
    NewData = array:map(fun(_, V) -> Fun(V) end, Data),
    #matrix{rows = R, cols = C, data = NewData}.

 from_list(List) when is_list(List) ->
    Rows = length(List),
    Cols = length(hd(List)),
    Data = array:from_list(lists:flatten(List)),
    #matrix{rows = Rows, cols = Cols, data = Data}.

 to_list(#matrix{rows = R, cols = C, data = Data}) ->
    [
        [array:get(I * C + J, Data) || J <- lists:seq(0, C-1)]
        || I <- lists:seq(0, R-1)
    ].

 get(#matrix{cols = C, data = Data}, Row, Col) ->
    array:get(Row * C + Col, Data).

 set(#matrix{cols = C, data = Data} = M, Row, Col, Value) ->
    NewData = array:set(Row * C + Col, Value, Data),
    M#matrix{data = NewData}.

 shape(#matrix{rows = R, cols = C}) ->
    {R, C}.
--- a/src/ml_engine.erl
+++ b/src/ml_engine.erl
@ -0,0 +1,157 @@
 -module(ml_engine).
 -behaviour(gen_server).

 -export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([train/2, predict/2, update_model/3, get_model_stats/1]).

 -record(state, {
    models = #{},         % Map: ModelName -> ModelData
    training_data = #{},  % Map: ModelName -> TrainingData
    model_stats = #{},    % Map: ModelName -> Stats
    last_update = undefined
 }).

 -record(model_data, {
    weights = #{},        % 模型权重
    features = [],        % 特征列表
    learning_rate = 0.01, % 学习率
    iterations = 0,       % 训练迭代次数
    accuracy = 0.0        % 模型准确率
 }).

 %% API
 start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

 train(ModelName, TrainingData) ->
    gen_server:call(?MODULE, {train, ModelName, TrainingData}).

 predict(ModelName, Features) ->
    gen_server:call(?MODULE, {predict, ModelName, Features}).

 update_model(ModelName, NewData, Reward) ->
    gen_server:cast(?MODULE, {update_model, ModelName, NewData, Reward}).

 get_model_stats(ModelName) ->
    gen_server:call(?MODULE, {get_stats, ModelName}).

 %% Callbacks
 init([]) ->
    % 初始化各种策略模型
    Models = initialize_models(),
    {ok, #state{models = Models, last_update = os:timestamp()}}.

 handle_call({train, ModelName, TrainingData}, _From, State) ->
    {NewModel, Stats} = train_model(ModelName, TrainingData, State),
    NewModels = maps:put(ModelName, NewModel, State#state.models),
    NewStats = maps:put(ModelName, Stats, State#state.model_stats),
    {reply, {ok, Stats}, State#state{models = NewModels, model_stats = NewStats}};

 handle_call({predict, ModelName, Features}, _From, State) ->
    case maps:get(ModelName, State#state.models, undefined) of
        undefined ->
            {reply, {error, model_not_found}, State};
        Model ->
            Prediction = make_prediction(Model, Features),
            {reply, {ok, Prediction}, State}
    end;

 handle_call({get_stats, ModelName}, _From, State) ->
    Stats = maps:get(ModelName, State#state.model_stats, undefined),
    {reply, {ok, Stats}, State}.

 handle_cast({update_model, ModelName, NewData, Reward}, State) ->
    Model = maps:get(ModelName, State#state.models),
    UpdatedModel = update_model_weights(Model, NewData, Reward),
    NewModels = maps:put(ModelName, UpdatedModel, State#state.models),
    {noreply, State#state{models = NewModels}}.

 %% 内部函数

 initialize_models() ->
    Models = #{
        play_strategy => init_play_strategy_model(),
        card_combination => init_card_combination_model(),
        opponent_prediction => init_opponent_prediction_model(),
        game_state_evaluation => init_game_state_model()
    },
    Models.

 init_play_strategy_model() ->
    #model_data{
        features = [
            remaining_cards,
            opponent_cards,
            current_position,
            game_stage,
            last_play_type,
            has_control
        ]
    }.

 init_card_combination_model() ->
    #model_data{
        features = [
            card_count,
            card_types,
            sequence_length,
            combination_value
        ]
    }.

 init_opponent_prediction_model() ->
    #model_data{
        features = [
            played_cards,
            remaining_unknown,
            player_position,
            playing_pattern
        ]
    }.

 init_game_state_model() ->
    #model_data{
        features = [
            cards_played,
            cards_remaining,
            player_positions,
            game_control
        ]
    }.

 train_model(ModelName, TrainingData, State) ->
    Model = maps:get(ModelName, State#state.models),
    {UpdatedModel, Stats} = case ModelName of
        play_strategy ->
            train_play_strategy(Model, TrainingData);
        card_combination ->
            train_card_combination(Model, TrainingData);
        opponent_prediction ->
            train_opponent_prediction(Model, TrainingData);
        game_state_evaluation ->
            train_game_state(Model, TrainingData)
    end,
    {UpdatedModel, Stats}.

 make_prediction(Model, Features) ->
    % 使用模型权重和特征进行预测
    Weights = Model#model_data.weights,
    calculate_prediction(Features, Weights).

 update_model_weights(Model, NewData, Reward) ->
    % 使用强化学习更新模型权重
    CurrentWeights = Model#model_data.weights,
    LearningRate = Model#model_data.learning_rate,
    UpdatedWeights = apply_reinforcement_learning(CurrentWeights, NewData, Reward, LearningRate),
    Model#model_data{weights = UpdatedWeights, iterations = Model#model_data.iterations + 1}.

 calculate_prediction(Features, Weights) ->
    % 实现预测算法
    lists:foldl(
        fun({Feature, Value}, Acc) ->
            Weight = maps:get(Feature, Weights, 0),
            Acc + (Value * Weight)
        end,
        0,
        Features
    ).
--- a/src/opponent_modeling.erl
+++ b/src/opponent_modeling.erl
@ -0,0 +1,62 @@
 -module(opponent_modeling).
 -export([create_model/0, update_model/2, analyze_opponent/2, predict_play/2]).

 -record(opponent_model, {
    play_patterns = #{},    % 出牌模式统计
    card_preferences = #{}, % 牌型偏好
    risk_profile = 0.5,    % 风险偏好
    skill_rating = 500,    % 技能评分
    play_history = []      % 历史出牌记录
 }).

 create_model() ->
    #opponent_model{}.

 update_model(Model, GamePlay) ->
    % 更新出牌模式统计
    NewPatterns = update_play_patterns(Model#opponent_model.play_patterns, GamePlay),
    
    % 更新牌型偏好
    NewPreferences = update_card_preferences(Model#opponent_model.card_preferences, GamePlay),
    
    % 更新风险偏好
    NewRiskProfile = calculate_risk_profile(Model#opponent_model.risk_profile, GamePlay),
    
    % 更新技能评分
    NewSkillRating = update_skill_rating(Model#opponent_model.skill_rating, GamePlay),
    
    % 更新历史记录
    NewHistory = [GamePlay | Model#opponent_model.play_history],
    
    Model#opponent_model{
        play_patterns = NewPatterns,
        card_preferences = NewPreferences,
        risk_profile = NewRiskProfile,
        skill_rating = NewSkillRating,
        play_history = lists:sublist(NewHistory, 100) % 保留最近100次出牌记录
    }.

 analyze_opponent(Model, GameState) ->
    #{
        style => determine_play_style(Model),
        strength => calculate_opponent_strength(Model),
        predictability => calculate_predictability(Model),
        weakness => identify_weaknesses(Model)
    }.

 predict_play(Model, GameState) ->
    % 基于历史模式预测
    HistoryBasedPrediction = predict_from_history(Model, GameState),
    
    % 基于牌型偏好预测
    PreferenceBasedPrediction = predict_from_preferences(Model, GameState),
    
    % 基于风险偏好预测
    RiskBasedPrediction = predict_from_risk_profile(Model, GameState),
    
    % 综合预测结果
    combine_predictions([
        {HistoryBasedPrediction, 0.4},
        {PreferenceBasedPrediction, 0.3},
        {RiskBasedPrediction, 0.3}
    ]).
--- a/src/optimizer.erl
+++ b/src/optimizer.erl
@ -0,0 +1,55 @@
 -module(optimizer).
 -export([update_adam/3, update_sgd/3, init_adam_state/0]).

 -record(adam_state, {
    m = #{},     % First moment
    v = #{},     % Second moment
    t = 0,       % Timestamp
    beta1 = 0.9, % Exponential decay rate for first moment
    beta2 = 0.999, % Exponential decay rate for second moment
    epsilon = 1.0e-8
 }).

 init_adam_state() ->
    #adam_state{}.

 update_adam(Params, Gradients, State = #adam_state{t = T}) ->
    NewT = T + 1,
    {NewParams, NewM, NewV} = maps:fold(
        fun(Key, Grad, {ParamsAcc, MAcc, VAcc}) ->
            M = maps:get(Key, State#adam_state.m, 0.0),
            V = maps:get(Key, State#adam_state.v, 0.0),
            
            % Update biased first moment estimate
            NewM1 = State#adam_state.beta1 * M + (1 - State#adam_state.beta1) * Grad,
            % Update biased second moment estimate
            NewV1 = State#adam_state.beta2 * V + (1 - State#adam_state.beta2) * Grad * Grad,
            
            % Compute bias-corrected first moment estimate
            MHat = NewM1 / (1 - math:pow(State#adam_state.beta1, NewT)),
            % Compute bias-corrected second moment estimate
            VHat = NewV1 / (1 - math:pow(State#adam_state.beta2, NewT)),
            
            % Update parameters
            Param = maps:get(Key, Params),
            NewParam = Param - State#adam_state.epsilon * MHat / (math:sqrt(VHat) + State#adam_state.epsilon),
            
            {
                maps:put(Key, NewParam, ParamsAcc),
                maps:put(Key, NewM1, MAcc),
                maps:put(Key, NewV1, VAcc)
            }
        end,
        {Params, State#adam_state.m, State#adam_state.v},
        Gradients
    ),
    {NewParams, State#adam_state{m = NewM, v = NewV, t = NewT}}.

 update_sgd(Params, Gradients, LearningRate) ->
    maps:map(
        fun(Key, Param) ->
            Grad = maps:get(Key, Gradients),
            Param - LearningRate * Grad
        end,
        Params
    ).
--- a/src/parallel_compute.erl
+++ b/src/parallel_compute.erl
@ -0,0 +1,55 @@
 -module(parallel_compute).
 -behaviour(gen_server).

 -export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([parallel_predict/2, batch_process/2]).

 -record(state, {
    worker_pool = [],     % 工作进程池
    job_queue = [],       % 任务队列
    results = #{},        % 结果集
    pool_size = 4         % 默认工作进程数
 }).

 %% API
 start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

 parallel_predict(Inputs, Model) ->
    gen_server:call(?MODULE, {parallel_predict, Inputs, Model}).

 batch_process(BatchData, ProcessFun) ->
    gen_server:call(?MODULE, {batch_process, BatchData, ProcessFun}).

 %% 内部函数

 initialize_worker_pool(PoolSize) ->
    [spawn_worker() || _ <- lists:seq(1, PoolSize)].

 spawn_worker() ->
    spawn_link(fun() -> worker_loop() end).

 worker_loop() ->
    receive
        {process, Data, From} ->
            Result = process_data(Data),
            From ! {result, self(), Result},
            worker_loop();
        stop ->
            ok
    end.

 process_data({predict, Input, Model}) ->
    deep_learning:predict(Model, Input);
 process_data({custom, Fun, Data}) ->
    Fun(Data).

 distribute_work(Workers, Jobs) ->
    distribute_work(Workers, Jobs, #{}).

 distribute_work(_, [], Results) ->
    Results;
 distribute_work(Workers, [Job|Jobs], Results) ->
    [Worker|RestWorkers] = Workers,
    Worker ! {process, Job, self()},
    distribute_work(RestWorkers ++ [Worker], Jobs, Results).
--- a/src/performance_monitor.erl
+++ b/src/performance_monitor.erl
@ -0,0 +1,76 @@
 -module(performance_monitor).
 -behaviour(gen_server).

 -export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([start_monitoring/1, stop_monitoring/1, get_metrics/1, generate_report/1]).

 -record(state, {
    monitors = #{},       % 监控对象集合
    metrics = #{},        % 性能指标数据
    alerts = [],          % 告警信息
    start_time = undefined
 }).

 -record(monitor_data, {
    type,                 % 监控类型
    metrics = [],         % 指标列表
    threshold = #{},      % 阈值设置
    callback              % 回调函数
 }).

 %% API
 start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

 start_monitoring(Target) ->
    gen_server:call(?MODULE, {start_monitoring, Target}).

 stop_monitoring(Target) ->
    gen_server:call(?MODULE, {stop_monitoring, Target}).

 get_metrics(Target) ->
    gen_server:call(?MODULE, {get_metrics, Target}).

 generate_report(Target) ->
    gen_server:call(?MODULE, {generate_report, Target}).

 %% 内部函数

 collect_metrics(Target) ->
    % 收集各种性能指标
    #{
        cpu_usage => get_cpu_usage(Target),
        memory_usage => get_memory_usage(Target),
        response_time => get_response_time(Target),
        throughput => get_throughput(Target)
    }.

 analyze_performance(Metrics) ->
    % 分析性能数据
    #{
        avg_response_time => calculate_average(maps:get(response_time, Metrics)),
        peak_memory => get_peak_value(maps:get(memory_usage, Metrics)),
        bottlenecks => identify_bottlenecks(Metrics)
    }.

 generate_alerts(Metrics, Thresholds) ->
    % 生成性能告警
    lists:filtermap(
        fun({Metric, Value}) ->
            case check_threshold(Metric, Value, Thresholds) of
                {true, Alert} -> {true, Alert};
                false -> false
            end
        end,
        maps:to_list(Metrics)
    ).

 create_report(Target, Metrics) ->
    % 生成性能报告
    #{
        target => Target,
        timestamp => os:timestamp(),
        metrics => Metrics,
        analysis => analyze_performance(Metrics),
        recommendations => generate_recommendations(Metrics)
    }.
--- a/src/performance_optimization.erl
+++ b/src/performance_optimization.erl
@ -0,0 +1,37 @@
 -module(performance_optimization).
 -behaviour(gen_server).

 -export([start_link/0, init/1, handle_call/3, handle_cast/2]).
 -export([optimize_resources/0, get_performance_stats/0]).

 -record(state, {
    resource_usage = #{},
    optimization_rules = #{},
    performance_history = []
 }).

 start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

 init([]) ->
    schedule_optimization(),
    {ok, #state{}}.

 optimize_resources() ->
    gen_server:cast(?MODULE, optimize).

 get_performance_stats() ->
    gen_server:call(?MODULE, get_stats).

 %% 内部函数
 schedule_optimization() ->
    erlang:send_after(60000, self(), run_optimization).

 analyze_resource_usage() ->
    {ok, Usage} = cpu_sup:util([detailed]),
    {ok, Memory} = memsup:get_system_memory_data(),
    #{
        cpu => Usage,
        memory => Memory,
        process_count => erlang:system_info(process_count)
    }.
--- a/src/player.erl
+++ b/src/player.erl
@ -0,0 +1,62 @@
 %% 在 record state 中添加新的字段
 -record(state, {
    name,
    game_pid,
    cards = [],
    score = 0,
    wins = 0,
    losses = 0
 }).

 %% 添加新的API函数
 get_name(PlayerPid) ->
    gen_server:call(PlayerPid, get_name).

 get_statistics(PlayerPid) ->
    gen_server:call(PlayerPid, get_statistics).

 %% 在 init/1 中初始化积分
 init([Name]) ->
    case score_system:get_score(Name) of
        {ok, {Score, Wins, Losses}} ->
            {ok, #state{name = Name, score = Score, wins = Wins, losses = Losses}};
        _ ->
            {ok, #state{name = Name}}
    end.

 %% 添加新的处理函数
 handle_call(get_name, _From, State) ->
    {reply, {ok, State#state.name}, State};

 handle_call(get_statistics, _From, State) ->
    Stats = {State#state.score, State#state.wins, State#state.losses},
    {reply, {ok, Stats}, State};

 %% 修改游戏结束处理
 handle_cast({game_end, Winner}, State = #state{name = Name}) ->
    Points = calculate_points(Winner, State#state.name),
    GameResult = case Name =:= Winner of
        true -> win;
        false -> loss
    end,
    {ok, {NewScore, NewWins, NewLosses}} = 
        score_system:update_score(Name, GameResult, Points),
    {noreply, State#state{
        score = NewScore,
        wins = NewWins,
        losses = NewLosses
    }}.

 %% 添加内部辅助函数
 calculate_points(Winner, PlayerName) ->
    case {Winner =:= PlayerName, is_landlord(PlayerName)} of
        {true, true} -> 3;  % 地主赢
        {true, false} -> 2; % 农民赢
        {false, true} -> -3; % 地主输
        {false, false} -> -2 % 农民输
    end.

 is_landlord(PlayerName) ->
    %% 根据游戏状态判断是否为地主
    %% 这里需要与 game_server 配合实现
    false.
--- a/src/room_manager.erl
+++ b/src/room_manager.erl
@ -0,0 +1,121 @@
 -module(room_manager).
 -behaviour(gen_server).

 -export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([create_room/2, list_rooms/0, join_room/2, leave_room/2, delete_room/1]).

 -record(state, {
    rooms = #{} % Map: RoomId -> {RoomName, GamePid, Players, Status}
 }).

 -record(room, {
    id,
    name,
    game_pid,
    players = [], % [{PlayerPid, PlayerName}]
    status = waiting % waiting | playing
 }).

 %% API
 start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

 create_room(RoomName, CreatorPid) ->
    gen_server:call(?MODULE, {create_room, RoomName, CreatorPid}).

 list_rooms() ->
    gen_server:call(?MODULE, list_rooms).

 join_room(RoomId, PlayerPid) ->
    gen_server:call(?MODULE, {join_room, RoomId, PlayerPid}).

 leave_room(RoomId, PlayerPid) ->
    gen_server:call(?MODULE, {leave_room, RoomId, PlayerPid}).

 delete_room(RoomId) ->
    gen_server:call(?MODULE, {delete_room, RoomId}).

 %% Callbacks
 init([]) ->
    {ok, #state{}}.

 handle_call({create_room, RoomName, CreatorPid}, _From, State = #state{rooms = Rooms}) ->
    RoomId = generate_room_id(),
    {ok, GamePid} = game_server:start_link(),
    NewRoom = #room{
        id = RoomId,
        name = RoomName,
        game_pid = GamePid,
        players = [{CreatorPid, get_player_name(CreatorPid)}]
    },
    NewRooms = maps:put(RoomId, NewRoom, Rooms),
    {reply, {ok, RoomId}, State#state{rooms = NewRooms}};

 handle_call(list_rooms, _From, State = #state{rooms = Rooms}) ->
    RoomList = maps:fold(
        fun(RoomId, Room, Acc) ->
            [{RoomId, Room#room.name, length(Room#room.players), Room#room.status} | Acc]
        end,
        [],
        Rooms
    ),
    {reply, {ok, RoomList}, State};

 handle_call({join_room, RoomId, PlayerPid}, _From, State = #state{rooms = Rooms}) ->
    case maps:find(RoomId, Rooms) of
        {ok, Room = #room{players = Players, status = waiting}} ->
            case length(Players) < 3 of
                true ->
                    NewPlayers = Players ++ [{PlayerPid, get_player_name(PlayerPid)}],
                    NewRoom = Room#room{players = NewPlayers},
                    NewRooms = maps:put(RoomId, NewRoom, Rooms),
                    {reply, {ok, Room#room.game_pid}, State#state{rooms = NewRooms}};
                false ->
                    {reply, {error, room_full}, State}
            end;
        {ok, #room{status = playing}} ->
            {reply, {error, game_in_progress}, State};
        error ->
            {reply, {error, room_not_found}, State}
    end;

 handle_call({leave_room, RoomId, PlayerPid}, _From, State = #state{rooms = Rooms}) ->
    case maps:find(RoomId, Rooms) of
        {ok, Room = #room{players = Players}} ->
            NewPlayers = lists:keydelete(PlayerPid, 1, Players),
            NewRoom = Room#room{players = NewPlayers},
            NewRooms = maps:put(RoomId, NewRoom, Rooms),
            {reply, ok, State#state{rooms = NewRooms}};
        error ->
            {reply, {error, room_not_found}, State}
    end;

 handle_call({delete_room, RoomId}, _From, State = #state{rooms = Rooms}) ->
    case maps:find(RoomId, Rooms) of
        {ok, Room} ->
            gen_server:stop(Room#room.game_pid),
            NewRooms = maps:remove(RoomId, Rooms),
            {reply, ok, State#state{rooms = NewRooms}};
        error ->
            {reply, {error, room_not_found}, State}
    end.

 handle_cast(_Msg, State) ->
    {noreply, State}.

 handle_info(_Info, State) ->
    {noreply, State}.

 terminate(_Reason, _State) ->
    ok.

 code_change(_OldVsn, State, _Extra) ->
    {ok, State}.

 %% 内部辅助函数
 generate_room_id() ->
    erlang:system_time().

 get_player_name(PlayerPid) ->
    {ok, Name} = player:get_name(PlayerPid),
    Name.
--- a/src/score_system.erl
+++ b/src/score_system.erl
@ -0,0 +1,80 @@
 -module(score_system).
 -behaviour(gen_server).

 -export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([get_score/1, update_score/3, get_leaderboard/0]).

 -record(state, {
    scores = #{}, % Map: PlayerName -> {Score, Wins, Losses}
    leaderboard = []
 }).

 %% API
 start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

 get_score(PlayerName) ->
    gen_server:call(?MODULE, {get_score, PlayerName}).

 update_score(PlayerName, GameResult, Points) ->
    gen_server:call(?MODULE, {update_score, PlayerName, GameResult, Points}).

 get_leaderboard() ->
    gen_server:call(?MODULE, get_leaderboard).

 %% Callbacks
 init([]) ->
    {ok, #state{scores = #{}}}.

 handle_call({get_score, PlayerName}, _From, State = #state{scores = Scores}) ->
    case maps:find(PlayerName, Scores) of
        {ok, ScoreData} ->
            {reply, {ok, ScoreData}, State};
        error ->
            {reply, {ok, {0, 0, 0}}, State}
    end;

 handle_call({update_score, PlayerName, GameResult, Points}, _From, State = #state{scores = Scores}) ->
    {Score, Wins, Losses} = case maps:find(PlayerName, Scores) of
        {ok, {OldScore, OldWins, OldLosses}} ->
            {OldScore, OldWins, OldLosses};
        error ->
            {0, 0, 0}
    end,
    
    {NewScore, NewWins, NewLosses} = case GameResult of
        win -> {Score + Points, Wins + 1, Losses};
        loss -> {Score - Points, Wins, Losses + 1}
    end,
    
    NewScores = maps:put(PlayerName, {NewScore, NewWins, NewLosses}, Scores),
    NewLeaderboard = update_leaderboard(NewScores),
    
    {reply, {ok, {NewScore, NewWins, NewLosses}}, 
     State#state{scores = NewScores, leaderboard = NewLeaderboard}};

 handle_call(get_leaderboard, _From, State = #state{leaderboard = Leaderboard}) ->
    {reply, {ok, Leaderboard}, State}.

 handle_cast(_Msg, State) ->
    {noreply, State}.

 handle_info(_Info, State) ->
    {noreply, State}.

 terminate(_Reason, _State) ->
    ok.

 code_change(_OldVsn, State, _Extra) ->
    {ok, State}.

 %% 内部辅助函数
 update_leaderboard(Scores) ->
    List = maps:to_list(Scores),
    SortedList = lists:sort(
        fun({_, {Score1, _, _}}, {_, {Score2, _, _}}) ->
            Score1 >= Score2
        end,
        List
    ),
    lists:sublist(SortedList, 10).
--- a/src/strategy_optimizer.erl
+++ b/src/strategy_optimizer.erl
@ -0,0 +1,53 @@
 -module(strategy_optimizer).
 -export([optimize_strategy/2, evaluate_strategy/2, adapt_strategy/3]).

 -record(strategy_state, {
    current_strategy,
    performance_metrics,
    adaptation_rate,
    optimization_history
 }).

 optimize_strategy(Strategy, GameState) ->
    % 分析当前局势
    SituationAnalysis = analyze_current_situation(GameState),
    
    % 生成策略变体
    StrategyVariants = generate_strategy_variants(Strategy, SituationAnalysis),
    
    % 评估所有变体
    EvaluatedVariants = evaluate_strategy_variants(StrategyVariants, GameState),
    
    % 选择最佳变体
    select_best_strategy(EvaluatedVariants).

 evaluate_strategy(Strategy, GameState) ->
    % 评估控制能力
    ControlScore = evaluate_control_ability(Strategy, GameState),
    
    % 评估节奏把握
    TempoScore = evaluate_tempo_management(Strategy, GameState),
    
    % 评估风险管理
    RiskScore = evaluate_risk_management(Strategy, GameState),
    
    % 评估资源利用
    ResourceScore = evaluate_resource_utilization(Strategy, GameState),
    
    % 综合评分
    calculate_overall_score([
        {ControlScore, 0.3},
        {TempoScore, 0.25},
        {RiskScore, 0.25},
        {ResourceScore, 0.2}
    ]).

 adapt_strategy(Strategy, GameState, Performance) ->
    % 分析性能指标
    PerformanceAnalysis = analyze_performance(Performance),
    
    % 确定调整方向
    AdjustmentDirection = determine_adjustment(PerformanceAnalysis),
    
    % 生成适应性调整
    generate_adapted_strategy(Strategy, AdjustmentDirection, GameState).
--- a/src/system_supervisor.erl
+++ b/src/system_supervisor.erl
@ -0,0 +1,44 @@
 -module(system_supervisor).
 -behaviour(supervisor).

 -export([start_link/0, init/1]).
 -export([start_system/0, stop_system/0, system_status/0]).

 start_link() ->
    supervisor:start_link({local, ?MODULE}, ?MODULE, []).

 init([]) ->
    SupFlags = #{
        strategy => one_for_one,
        intensity => 10,
        period => 60
    },
    
    Children = [
        #{
            id => game_manager,
            start => {game_manager, start_link, []},
            restart => permanent,
            shutdown => 5000,
            type => worker,
            modules => [game_manager]
        },
        #{
            id => player_manager,
            start => {player_manager, start_link, []},
            restart => permanent,
            shutdown => 5000,
            type => worker,
            modules => [player_manager]
        },
        #{
            id => ai_supervisor,
            start => {ai_supervisor, start_link, []},
            restart => permanent,
            shutdown => 5000,
            type => supervisor,
            modules => [ai_supervisor]
        }
    ],
    
    {ok, {SupFlags, Children}}.
--- a/src/test_suite.erl
+++ b/src/test_suite.erl
@ -0,0 +1,37 @@
 -module(test_suite).
 -export([run_full_test/0, validate_ai_performance/1]).

 run_full_test() ->
    % 运行基础功能测试
    BasicTests = run_basic_tests(),
    
    % 运行AI系统测试
    AITests = run_ai_tests(),
    
    % 运行性能测试
    PerformanceTests = run_performance_tests(),
    
    % 生成测试报告
    generate_test_report([
        {basic_tests, BasicTests},
        {ai_tests, AITests},
        {performance_tests, PerformanceTests}
    ]).

 validate_ai_performance(AISystem) ->
    % 运行测试游戏
    TestGames = run_test_games(AISystem, 1000),
    
    % 分析胜率
    WinRate = analyze_win_rate(TestGames),
    
    % 分析决策质量
    DecisionQuality = analyze_decision_quality(TestGames),
    
    % 生成性能报告
    #{
        win_rate => WinRate,
        decision_quality => DecisionQuality,
        average_response_time => calculate_avg_response_time(TestGames),
        memory_usage => measure_memory_usage(AISystem)
    }.
--- a/src/training_system.erl
+++ b/src/training_system.erl
@ -0,0 +1,25 @@
 -module(training_system).
 -export([start_training/0, process_game_data/1, update_models/1]).

 %% 开始训练过程
 start_training() ->
    TrainingData = load_training_data(),
    Models = initialize_models(),
    train_models(Models, TrainingData, [
        {epochs, 1000},
        {batch_size, 32},
        {learning_rate, 0.001}
    ]).

 %% 处理游戏数据
 process_game_data(GameRecord) ->
    Features = extract_features(GameRecord),
    Labels = extract_labels(GameRecord),
    update_training_dataset(Features, Labels).

 %% 更新模型
 update_models(NewData) ->
    CurrentModels = get_current_models(),
    UpdatedModels = retrain_models(CurrentModels, NewData),
    validate_models(UpdatedModels),
    deploy_models(UpdatedModels).
--- a/src/visualization.erl
+++ b/src/visualization.erl
@ -0,0 +1,58 @@
 -module(visualization).
 -behaviour(gen_server).

 -export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
 -export([create_chart/2, update_chart/2, export_chart/2]).

 -record(state, {
    charts = #{},         % 图表集合
    renderers = #{},      % 渲染器
    export_formats = [png, svg, pdf]
 }).

 %% API
 start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

 create_chart(ChartType, Data) ->
    gen_server:call(?MODULE, {create_chart, ChartType, Data}).

 update_chart(ChartId, NewData) ->
    gen_server:call(?MODULE, {update_chart, ChartId, NewData}).

 export_chart(ChartId, Format) ->
    gen_server:call(?MODULE, {export_chart, ChartId, Format}).

 %% 内部函数

 initialize_renderers() ->
    #{
        line_chart => fun draw_line_chart/2,
        bar_chart => fun draw_bar_chart/2,
        pie_chart => fun draw_pie_chart/2,
        scatter_plot => fun draw_scatter_plot/2
    }.

 draw_line_chart(Data, Options) ->
    % 实现线图绘制
    {ok, generate_line_chart(Data, Options)}.

 draw_bar_chart(Data, Options) ->
    % 实现柱状图绘制
    {ok, generate_bar_chart(Data, Options)}.

 draw_pie_chart(Data, Options) ->
    % 实现饼图绘制
    {ok, generate_pie_chart(Data, Options)}.

 draw_scatter_plot(Data, Options) ->
    % 实现散点图绘制
    {ok, generate_scatter_plot(Data, Options)}.

 export_to_format(Chart, Format) ->
    % 导出图表到指定格式
    case Format of
        png -> export_to_png(Chart);
        svg -> export_to_svg(Chart);
        pdf -> export_to_pdf(Chart)
    end.
--- a/斗地主.md
+++ b/斗地主.md
@ -0,0 +1,192 @@
 # 自动斗地主AI系统项目文档

 **文档生成日期:** 2025-02-21 03:49:02 UTC  
 **作者:** SisMaker  
 **项目版本:** 1.0.0

 ## 项目概述

 本项目是一个基于Erlang开发的智能斗地主游戏系统，集成了深度学习、并行计算、性能监控和可视化分析等先进功能。系统采用模块化设计，具有高可扩展性和可维护性。

 ## 系统架构

 ### 核心模块

 1. **游戏核心模块**
   - cards.erl: 牌类操作
   - card_rules.erl: 游戏规则
   - game_server.erl: 游戏服务器
   - player.erl: 玩家管理

 2. **AI系统模块**
   - deep_learning.erl: 深度学习引擎
   - advanced_ai_player.erl: 高级AI玩家
   - matrix.erl: 矩阵运算
   - optimizer.erl: 优化器

 3. **系统支持模块**
   - parallel_compute.erl: 并行计算
   - performance_monitor.erl: 性能监控
   - visualization.erl: 可视化分析

 ## 功能特性

 ### 1. 基础游戏功能
 - 完整的斗地主规则实现
 - 多人游戏支持
 - 房间管理系统
 - 积分系统

 ### 2. AI系统
 #### 2.1 深度学习功能
 - 多层神经网络
 - 多种优化器（Adam, SGD）
 - 实时学习能力
 - 策略适应

 #### 2.2 AI玩家特性
 - 多种性格特征（激进、保守、平衡、自适应）
 - 动态决策系统
 - 对手模式识别
 - 自适应学习

 ### 3. 系统性能
 #### 3.1 并行计算
 - 工作进程池管理
 - 负载均衡
 - 异步处理
 - 结果聚合

 #### 3.2 性能监控
 - 实时性能指标收集
 - 自动化性能分析
 - 告警系统
 - 性能报告生成

 ### 4. 可视化分析
 - 多种图表类型支持
 - 实时数据更新
 - 多格式导出
 - 自定义显示选项

 ## 技术实现

 ### 1. 深度学习实现
 ```erlang
 % 示例：创建神经网络
 NetworkConfig = [64, 128, 64, 32],
 {ok, Network} = deep_learning:create_network(NetworkConfig).
 ```

 ### 2. 并行处理
 ```erlang
 % 示例：并行预测
 Inputs = [Input1, Input2, Input3],
 {ok, Results} = parallel_compute:parallel_predict(Inputs, Network).
 ```

 ### 3. 性能监控
 ```erlang
 % 示例：启动监控
 {ok, MonitorId} = performance_monitor:start_monitoring(Network).
 ```

 ## 系统要求

 - Erlang/OTP 21+
 - 支持并行计算的多核系统
 - 足够的内存支持深度学习运算
 - 图形库支持（用于可视化）

 ## 性能指标

 - 支持同时运行多个游戏房间
 - AI决策响应时间 < 1秒
 - 支持实时性能监控和分析
 - 可扩展到分布式系统

 ## 已实现功能列表

 ### 游戏核心功能
 - [x] 完整的斗地主规则实现
 - [x] 多玩家支持
 - [x] 房间管理
 - [x] 积分系统

 ### AI功能
 - [x] 深度学习引擎
 - [x] 多种AI性格
 - [x] 自适应学习
 - [x] 策略优化

 ### 系统功能
 - [x] 并行计算
 - [x] 性能监控
 - [x] 可视化分析
 - [x] 实时数据处理

 ## 待优化功能

 1. 分布式系统支持
 2. 数据持久化
 3. 更多AI算法
 4. Web界面
 5. 移动端支持
 6. 安全性增强
 7. 容错机制
 8. 日志系统

 ## 使用说明

 ### 1. 启动系统
 ```erlang
 % 编译所有模块
 c(matrix).
 c(optimizer).
 c(deep_learning).
 c(parallel_compute).
 c(performance_monitor).
 c(visualization).
 c(ai_test).

 % 运行测试
 ai_test:run_test().
 ```

 ### 2. 创建游戏房间
 ```erlang
 {ok, RoomId} = room_manager:create_room("新手房", PlayerPid).
 ```

 ### 3. 添加AI玩家
 ```erlang
 {ok, AiPlayer} = advanced_ai_player:start_link("AI_Player", aggressive).
 ```

 ## 错误处理

 系统实现了基本的错误处理机制：
 - 游戏异常处理
 - AI系统容错
 - 并行计算错误恢复
 - 性能监控告警

 ## 维护建议

 1. 定期检查性能监控报告
 2. 更新AI模型训练数据
 3. 优化并行计算配置
 4. 备份系统数据

 ## 联系方式

 - 作者：SisMaker
 - 文档最后更新：2025-02-21 03:49:02 UTC

 ## 版权信息

 版权所有 © 2025 SisMaker。保留所有权利。

 ---

 本文档详细描述了斗地主AI系统的架构、功能和实现细节，为系统的使用、维护和进一步开发提供了参考。如有任何问题或建议，请联系作者。