commit fca2a74f4a5ecf311d93d815dab0d4583d21a967 Author: SisMaker <1713699517@qq.com> Date: Fri Feb 21 15:03:23 2025 +0800 ft: 初始化提交 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f1c4554 --- /dev/null +++ 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 +*~ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..36d37a9 --- /dev/null +++ b/LICENSE @@ -0,0 +1,191 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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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 \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..d24623b --- /dev/null +++ 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 + diff --git a/include/game_records.hrl b/include/game_records.hrl new file mode 100644 index 0000000..3025a2f --- /dev/null +++ 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 +}). \ No newline at end of file diff --git a/rebar.config b/rebar.config new file mode 100644 index 0000000..be2972a --- /dev/null +++ b/rebar.config @@ -0,0 +1,7 @@ +{erl_opts, [debug_info]}. +{deps, []}. + +{shell, [ + % {config, "config/sys.config"}, + {apps, [cardSrv]} +]}. diff --git a/src/advanced_ai_player.erl b/src/advanced_ai_player.erl new file mode 100644 index 0000000..8b44928 --- /dev/null +++ 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 + }. \ No newline at end of file diff --git a/src/advanced_ai_strategy.erl b/src/advanced_ai_strategy.erl new file mode 100644 index 0000000..7c37645 --- /dev/null +++ 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). \ No newline at end of file diff --git a/src/advanced_learning.erl b/src/advanced_learning.erl new file mode 100644 index 0000000..8716185 --- /dev/null +++ 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. \ No newline at end of file diff --git a/src/ai_core.erl b/src/ai_core.erl new file mode 100644 index 0000000..6ff0b4a --- /dev/null +++ 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 + ). \ No newline at end of file diff --git a/src/ai_optimizer.erl b/src/ai_optimizer.erl new file mode 100644 index 0000000..465d18c --- /dev/null +++ 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). \ No newline at end of file diff --git a/src/ai_player.erl b/src/ai_player.erl new file mode 100644 index 0000000..dfb952f --- /dev/null +++ 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. \ No newline at end of file diff --git a/src/ai_strategy.erl b/src/ai_strategy.erl new file mode 100644 index 0000000..ae42d1c --- /dev/null +++ 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. \ No newline at end of file diff --git a/src/ai_test.erl b/src/ai_test.erl new file mode 100644 index 0000000..78d53e7 --- /dev/null +++ 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]. \ No newline at end of file diff --git a/src/auto_player.erl b/src/auto_player.erl new file mode 100644 index 0000000..67ceb5d --- /dev/null +++ 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. \ No newline at end of file diff --git a/src/cardSrv.app.src b/src/cardSrv.app.src new file mode 100644 index 0000000..7cf6ef2 --- /dev/null +++ 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, []} + ]}. diff --git a/src/cardSrv_app.erl b/src/cardSrv_app.erl new file mode 100644 index 0000000..ec52ead --- /dev/null +++ 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 diff --git a/src/cardSrv_sup.erl b/src/cardSrv_sup.erl new file mode 100644 index 0000000..999cbd2 --- /dev/null +++ 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 diff --git a/src/card_rules.erl b/src/card_rules.erl new file mode 100644 index 0000000..5148119 --- /dev/null +++ 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. \ No newline at end of file diff --git a/src/cards.erl b/src/cards.erl new file mode 100644 index 0000000..54c19c9 --- /dev/null +++ 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). \ No newline at end of file diff --git a/src/decision_engine.erl b/src/decision_engine.erl new file mode 100644 index 0000000..b29ff7f --- /dev/null +++ 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} + ]). \ No newline at end of file diff --git a/src/deep_learning.erl b/src/deep_learning.erl new file mode 100644 index 0000000..00dceb8 --- /dev/null +++ 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). \ No newline at end of file diff --git a/src/game_core.erl b/src/game_core.erl new file mode 100644 index 0000000..bfe3e66 --- /dev/null +++ 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))). \ No newline at end of file diff --git a/src/game_logic.erl b/src/game_logic.erl new file mode 100644 index 0000000..83a666d --- /dev/null +++ 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. \ No newline at end of file diff --git a/src/game_manager.erl b/src/game_manager.erl new file mode 100644 index 0000000..5dcd25a --- /dev/null +++ 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. \ No newline at end of file diff --git a/src/game_server.erl b/src/game_server.erl new file mode 100644 index 0000000..734bcaf --- /dev/null +++ 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. \ No newline at end of file diff --git a/src/matrix.erl b/src/matrix.erl new file mode 100644 index 0000000..cb445e1 --- /dev/null +++ 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}. \ No newline at end of file diff --git a/src/ml_engine.erl b/src/ml_engine.erl new file mode 100644 index 0000000..d716b19 --- /dev/null +++ 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 + ). \ No newline at end of file diff --git a/src/opponent_modeling.erl b/src/opponent_modeling.erl new file mode 100644 index 0000000..88454a9 --- /dev/null +++ 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} + ]). \ No newline at end of file diff --git a/src/optimizer.erl b/src/optimizer.erl new file mode 100644 index 0000000..f25f6da --- /dev/null +++ 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 + ). \ No newline at end of file diff --git a/src/parallel_compute.erl b/src/parallel_compute.erl new file mode 100644 index 0000000..50ce221 --- /dev/null +++ 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). \ No newline at end of file diff --git a/src/performance_monitor.erl b/src/performance_monitor.erl new file mode 100644 index 0000000..dbf9622 --- /dev/null +++ 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) + }. \ No newline at end of file diff --git a/src/performance_optimization.erl b/src/performance_optimization.erl new file mode 100644 index 0000000..d83cc63 --- /dev/null +++ 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) + }. \ No newline at end of file diff --git a/src/player.erl b/src/player.erl new file mode 100644 index 0000000..fa6fb44 --- /dev/null +++ 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. \ No newline at end of file diff --git a/src/room_manager.erl b/src/room_manager.erl new file mode 100644 index 0000000..9aa4ee7 --- /dev/null +++ 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. \ No newline at end of file diff --git a/src/score_system.erl b/src/score_system.erl new file mode 100644 index 0000000..ffb4218 --- /dev/null +++ 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). \ No newline at end of file diff --git a/src/strategy_optimizer.erl b/src/strategy_optimizer.erl new file mode 100644 index 0000000..34c4530 --- /dev/null +++ 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). \ No newline at end of file diff --git a/src/system_supervisor.erl b/src/system_supervisor.erl new file mode 100644 index 0000000..5cbb681 --- /dev/null +++ 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}}. \ No newline at end of file diff --git a/src/test_suite.erl b/src/test_suite.erl new file mode 100644 index 0000000..2db805c --- /dev/null +++ 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) + }. \ No newline at end of file diff --git a/src/training_system.erl b/src/training_system.erl new file mode 100644 index 0000000..1d9a3ae --- /dev/null +++ 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). \ No newline at end of file diff --git a/src/visualization.erl b/src/visualization.erl new file mode 100644 index 0000000..ea98484 --- /dev/null +++ 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. \ No newline at end of file diff --git a/斗地主.md b/斗地主.md new file mode 100644 index 0000000..1592d1a --- /dev/null +++ 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。 \ No newline at end of file