-module(advanced_autonomous_agent). -behaviour(gen_server). %% Advanced Autonomous Agent %% Comprehensive autonomous agent that integrates all cognitive components: %% - Autonomous agency with environmental perception and goal formation %% - Dynamic knowledge graph construction and exploration %% - Deep reasoning with multi-level cognition %% - Environmental learning and adaptation %% - Autonomous goal planning and execution %% - Active exploration and discovery %% - Coordination between all cognitive processes -export([start_link/1, % Core agent functions process_environmental_input/2, autonomous_decision_making/2, execute_autonomous_action/2, reflect_on_experience/2, plan_autonomous_goals/2, explore_environment/2, learn_from_interaction/3, reason_about_situation/3, update_knowledge_graph/3, % Cognitive coordination coordinate_cognitive_processes/2, integrate_cognitive_insights/2, balance_cognitive_resources/2, optimize_cognitive_performance/2, % Advanced capabilities emergent_behavior_analysis/2, meta_cognitive_reflection/2, adaptive_strategy_formation/2, creative_problem_solving/3, % Agent interaction and collaboration initiate_agent_collaboration/3, coordinate_multi_agent_task/3, share_knowledge_with_agents/3, learn_from_agent_interaction/3]). -export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]). %% Advanced agent state structure -record(advanced_agent_state, { agent_id, % Unique agent identifier agent_config = #{}, % Agent configuration % Cognitive component PIDs autonomous_agency_pid, % Core autonomous agency process knowledge_graph_pid, % Dynamic knowledge graph process reasoning_engine_pid, % Deep reasoning engine process learning_engine_pid, % Environmental learning engine process goal_planner_pid, % Autonomous goal planner process exploration_engine_pid, % Active exploration engine process % Integrated cognitive state cognitive_state = #{}, % Current cognitive state environmental_model = #{}, % Current environmental understanding active_goals = [], % Currently active goals active_explorations = [], % Currently active explorations knowledge_insights = [], % Recent knowledge insights reasoning_conclusions = [], % Recent reasoning conclusions learning_adaptations = [], % Recent learning adaptations % Coordination and performance cognitive_coordination_state = #{}, % State of cognitive coordination cognitive_performance_metrics = #{}, % Performance metrics for each component cognitive_resource_allocation = #{}, % Resource allocation between components cognitive_synchronization = #{}, % Synchronization state between components % Meta-cognitive capabilities meta_cognitive_awareness = #{}, % Self-awareness of cognitive processes emergent_behaviors = [], % Detected emergent behaviors adaptive_strategies = [], % Developed adaptive strategies creative_insights = [], % Creative insights and innovations % Agent interaction and collaboration agent_network = #{}, % Network of connected agents collaboration_state = #{}, % State of ongoing collaborations shared_knowledge = #{}, % Knowledge shared with other agents interaction_history = [], % History of agent interactions % Temporal and contextual tracking agent_creation_time, % When agent was created last_cognitive_cycle, % Last cognitive processing cycle experience_timeline = [], % Timeline of agent experiences contextual_memory = #{}, % Contextual memory and associations % Advanced capabilities state consciousness_simulation = #{}, % Simulated consciousness state free_will_simulation = #{}, % Simulated autonomous decision making personality_model = #{}, % Agent's developing personality values_and_ethics = #{} % Agent's values and ethical framework }). %%==================================================================== %% API functions %%==================================================================== start_link(Config) -> AgentId = maps:get(agent_id, Config, generate_agent_id()), io:format("[ADVANCED_AGENT] Starting advanced autonomous agent ~p~n", [AgentId]), gen_server:start_link(?MODULE, [AgentId, Config], []). %% Core agent functions process_environmental_input(AgentPid, EnvironmentalInput) -> gen_server:call(AgentPid, {process_environmental_input, EnvironmentalInput}). autonomous_decision_making(AgentPid, DecisionContext) -> gen_server:call(AgentPid, {autonomous_decision_making, DecisionContext}). execute_autonomous_action(AgentPid, Action) -> gen_server:call(AgentPid, {execute_autonomous_action, Action}). reflect_on_experience(AgentPid, Experience) -> gen_server:call(AgentPid, {reflect_on_experience, Experience}). plan_autonomous_goals(AgentPid, GoalContext) -> gen_server:call(AgentPid, {plan_autonomous_goals, GoalContext}). explore_environment(AgentPid, ExplorationScope) -> gen_server:call(AgentPid, {explore_environment, ExplorationScope}). learn_from_interaction(AgentPid, Interaction, Context) -> gen_server:call(AgentPid, {learn_from_interaction, Interaction, Context}). reason_about_situation(AgentPid, Situation, ReasoningType) -> gen_server:call(AgentPid, {reason_about_situation, Situation, ReasoningType}). update_knowledge_graph(AgentPid, Knowledge, Context) -> gen_server:call(AgentPid, {update_knowledge_graph, Knowledge, Context}). %% Cognitive coordination coordinate_cognitive_processes(AgentPid, CoordinationRequest) -> gen_server:call(AgentPid, {coordinate_cognitive_processes, CoordinationRequest}). integrate_cognitive_insights(AgentPid, IntegrationRequest) -> gen_server:call(AgentPid, {integrate_cognitive_insights, IntegrationRequest}). balance_cognitive_resources(AgentPid, ResourceContext) -> gen_server:call(AgentPid, {balance_cognitive_resources, ResourceContext}). optimize_cognitive_performance(AgentPid, OptimizationObjective) -> gen_server:call(AgentPid, {optimize_cognitive_performance, OptimizationObjective}). %% Advanced capabilities emergent_behavior_analysis(AgentPid, BehaviorContext) -> gen_server:call(AgentPid, {emergent_behavior_analysis, BehaviorContext}). meta_cognitive_reflection(AgentPid, ReflectionScope) -> gen_server:call(AgentPid, {meta_cognitive_reflection, ReflectionScope}). adaptive_strategy_formation(AgentPid, StrategyContext) -> gen_server:call(AgentPid, {adaptive_strategy_formation, StrategyContext}). creative_problem_solving(AgentPid, Problem, CreativityContext) -> gen_server:call(AgentPid, {creative_problem_solving, Problem, CreativityContext}). %% Agent interaction and collaboration initiate_agent_collaboration(AgentPid, TargetAgent, CollaborationPurpose) -> gen_server:call(AgentPid, {initiate_agent_collaboration, TargetAgent, CollaborationPurpose}). coordinate_multi_agent_task(AgentPid, Task, AgentGroup) -> gen_server:call(AgentPid, {coordinate_multi_agent_task, Task, AgentGroup}). share_knowledge_with_agents(AgentPid, Knowledge, TargetAgents) -> gen_server:call(AgentPid, {share_knowledge_with_agents, Knowledge, TargetAgents}). learn_from_agent_interaction(AgentPid, Interaction, LearningContext) -> gen_server:call(AgentPid, {learn_from_agent_interaction, Interaction, LearningContext}). %%==================================================================== %% gen_server callbacks %%==================================================================== init([AgentId, Config]) -> process_flag(trap_exit, true), io:format("[ADVANCED_AGENT] Initializing advanced autonomous agent ~p~n", [AgentId]), % Start all cognitive component processes {ok, AutonomousAgencyPid} = start_cognitive_component(autonomous_agency, AgentId, Config), {ok, KnowledgeGraphPid} = start_cognitive_component(dynamic_knowledge_graph, AgentId, Config), {ok, ReasoningEnginePid} = start_cognitive_component(deep_reasoning_engine, AgentId, Config), {ok, LearningEnginePid} = start_cognitive_component(environmental_learning_engine, AgentId, Config), {ok, GoalPlannerPid} = start_cognitive_component(autonomous_goal_planner, AgentId, Config), {ok, ExplorationEnginePid} = start_cognitive_component(active_exploration_engine, AgentId, Config), % Initialize agent state State = #advanced_agent_state{ agent_id = AgentId, agent_config = Config, autonomous_agency_pid = AutonomousAgencyPid, knowledge_graph_pid = KnowledgeGraphPid, reasoning_engine_pid = ReasoningEnginePid, learning_engine_pid = LearningEnginePid, goal_planner_pid = GoalPlannerPid, exploration_engine_pid = ExplorationEnginePid, cognitive_state = initialize_cognitive_state(Config), cognitive_coordination_state = initialize_coordination_state(), cognitive_performance_metrics = initialize_performance_metrics(), cognitive_resource_allocation = initialize_resource_allocation(), meta_cognitive_awareness = initialize_meta_cognitive_awareness(), consciousness_simulation = initialize_consciousness_simulation(Config), personality_model = initialize_personality_model(Config), values_and_ethics = initialize_values_and_ethics(Config), agent_creation_time = erlang:system_time(second) }, % Start cognitive coordination cycles schedule_cognitive_coordination_cycle(), schedule_meta_cognitive_cycle(), schedule_performance_optimization_cycle(), schedule_consciousness_simulation_cycle(), % Register with agent registry if available register_with_agent_network(AgentId, State), {ok, State}. handle_call({process_environmental_input, EnvironmentalInput}, _From, State) -> io:format("[ADVANCED_AGENT] Processing environmental input~n"), % Coordinate cognitive processing of environmental input ProcessingResult = coordinate_environmental_processing(EnvironmentalInput, State), % Update environmental model UpdatedEnvironmentalModel = update_environmental_model(EnvironmentalInput, ProcessingResult, State#advanced_agent_state.environmental_model), % Trigger autonomous responses AutonomousResponses = generate_autonomous_responses(EnvironmentalInput, ProcessingResult, State), % Update cognitive state UpdatedCognitiveState = update_cognitive_state_from_input(EnvironmentalInput, ProcessingResult, State#advanced_agent_state.cognitive_state), % Record experience Experience = create_experience_record(environmental_input, EnvironmentalInput, ProcessingResult), UpdatedTimeline = [Experience | State#advanced_agent_state.experience_timeline], NewState = State#advanced_agent_state{ environmental_model = UpdatedEnvironmentalModel, cognitive_state = UpdatedCognitiveState, experience_timeline = UpdatedTimeline }, Result = #{ processing_result => ProcessingResult, autonomous_responses => AutonomousResponses, environmental_model_updates => UpdatedEnvironmentalModel }, {reply, {ok, Result}, NewState}; handle_call({autonomous_decision_making, DecisionContext}, _From, State) -> io:format("[ADVANCED_AGENT] Autonomous decision making~n"), % Gather information from all cognitive components CognitiveInputs = gather_cognitive_inputs_for_decision(DecisionContext, State), % Perform multi-level reasoning about decision ReasoningResult = coordinate_decision_reasoning(DecisionContext, CognitiveInputs, State), % Consider goals and values in decision making GoalAlignmentAnalysis = analyze_decision_goal_alignment(DecisionContext, ReasoningResult, State), EthicalAnalysis = analyze_decision_ethics(DecisionContext, ReasoningResult, State), % Generate decision alternatives DecisionAlternatives = generate_decision_alternatives(DecisionContext, ReasoningResult, State), % Evaluate alternatives using multiple criteria AlternativeEvaluations = evaluate_decision_alternatives(DecisionAlternatives, GoalAlignmentAnalysis, EthicalAnalysis, State), % Select optimal decision using autonomous choice mechanism SelectedDecision = autonomous_decision_selection(AlternativeEvaluations, State), % Simulate free will in decision making FreeWillSimulation = simulate_free_will_in_decision(SelectedDecision, DecisionContext, State), % Update consciousness simulation UpdatedConsciousness = update_consciousness_from_decision(SelectedDecision, FreeWillSimulation, State#advanced_agent_state.consciousness_simulation), NewState = State#advanced_agent_state{consciousness_simulation = UpdatedConsciousness}, Result = #{ selected_decision => SelectedDecision, reasoning_result => ReasoningResult, goal_alignment => GoalAlignmentAnalysis, ethical_analysis => EthicalAnalysis, free_will_simulation => FreeWillSimulation, decision_confidence => calculate_decision_confidence(AlternativeEvaluations) }, {reply, {ok, Result}, NewState}; handle_call({execute_autonomous_action, Action}, _From, State) -> io:format("[ADVANCED_AGENT] Executing autonomous action: ~p~n", [Action]), % Coordinate action execution across cognitive components ExecutionPlan = coordinate_action_execution(Action, State), % Execute action with monitoring ExecutionResult = execute_action_with_monitoring(ExecutionPlan, State), % Learn from action execution LearningFromAction = coordinate_learning_from_action(Action, ExecutionResult, State), % Update knowledge graph with action outcomes KnowledgeUpdates = update_knowledge_from_action(Action, ExecutionResult, State), % Reflect on action effectiveness ActionReflection = coordinate_action_reflection(Action, ExecutionResult, State), % Update agent's experience and capabilities UpdatedCapabilities = update_capabilities_from_action(Action, ExecutionResult, ActionReflection, State#advanced_agent_state.cognitive_state), % Record action in experience timeline ActionExperience = create_experience_record(autonomous_action, Action, ExecutionResult), UpdatedTimeline = [ActionExperience | State#advanced_agent_state.experience_timeline], NewState = State#advanced_agent_state{ cognitive_state = UpdatedCapabilities, experience_timeline = UpdatedTimeline }, Result = #{ execution_result => ExecutionResult, learning_insights => LearningFromAction, knowledge_updates => KnowledgeUpdates, action_reflection => ActionReflection }, {reply, {ok, Result}, NewState}; handle_call({reflect_on_experience, Experience}, _From, State) -> io:format("[ADVANCED_AGENT] Reflecting on experience~n"), % Coordinate meta-cognitive reflection across components ReflectionResults = coordinate_meta_cognitive_reflection(Experience, State), % Analyze patterns in agent's experience ExperiencePatterns = analyze_experience_patterns(Experience, State#advanced_agent_state.experience_timeline), % Generate insights from reflection ReflectionInsights = generate_reflection_insights(ReflectionResults, ExperiencePatterns, State), % Update meta-cognitive awareness UpdatedMetaCognitive = update_meta_cognitive_awareness(ReflectionInsights, State#advanced_agent_state.meta_cognitive_awareness), % Update personality model based on reflection UpdatedPersonality = update_personality_from_reflection(ReflectionInsights, State#advanced_agent_state.personality_model), NewState = State#advanced_agent_state{ meta_cognitive_awareness = UpdatedMetaCognitive, personality_model = UpdatedPersonality }, Result = #{ reflection_results => ReflectionResults, experience_patterns => ExperiencePatterns, reflection_insights => ReflectionInsights, meta_cognitive_updates => UpdatedMetaCognitive }, {reply, {ok, Result}, NewState}; handle_call({coordinate_cognitive_processes, CoordinationRequest}, _From, State) -> io:format("[ADVANCED_AGENT] Coordinating cognitive processes~n"), % Analyze coordination requirements CoordinationAnalysis = analyze_coordination_requirements(CoordinationRequest, State), % Coordinate between cognitive components CoordinationResult = execute_cognitive_coordination(CoordinationAnalysis, State), % Update coordination state UpdatedCoordinationState = update_coordination_state(CoordinationResult, State#advanced_agent_state.cognitive_coordination_state), % Optimize cognitive resource allocation OptimizedResourceAllocation = optimize_resource_allocation(CoordinationResult, State), NewState = State#advanced_agent_state{ cognitive_coordination_state = UpdatedCoordinationState, cognitive_resource_allocation = OptimizedResourceAllocation }, {reply, {ok, CoordinationResult}, NewState}; handle_call({emergent_behavior_analysis, BehaviorContext}, _From, State) -> io:format("[ADVANCED_AGENT] Analyzing emergent behavior~n"), % Analyze agent's behavior patterns for emergence BehaviorPatterns = analyze_agent_behavior_patterns(State), % Detect emergent behaviors DetectedEmergence = detect_emergent_behaviors(BehaviorPatterns, BehaviorContext, State), % Analyze emergence mechanisms EmergenceMechanisms = analyze_emergence_mechanisms(DetectedEmergence, State), % Update emergent behavior tracking UpdatedEmergentBehaviors = update_emergent_behavior_tracking(DetectedEmergence, State#advanced_agent_state.emergent_behaviors), NewState = State#advanced_agent_state{emergent_behaviors = UpdatedEmergentBehaviors}, Result = #{ behavior_patterns => BehaviorPatterns, detected_emergence => DetectedEmergence, emergence_mechanisms => EmergenceMechanisms }, {reply, {ok, Result}, NewState}; handle_call({creative_problem_solving, Problem, CreativityContext}, _From, State) -> io:format("[ADVANCED_AGENT] Creative problem solving~n"), % Coordinate creative cognitive processes CreativeProcesses = coordinate_creative_processes(Problem, CreativityContext, State), % Generate creative solutions CreativeSolutions = generate_creative_solutions(Problem, CreativeProcesses, State), % Evaluate creative solutions SolutionEvaluations = evaluate_creative_solutions(CreativeSolutions, Problem, State), % Select and refine best creative solution RefinedSolution = refine_creative_solution(SolutionEvaluations, State), % Learn from creative process CreativeLearning = learn_from_creative_process(Problem, RefinedSolution, CreativeProcesses, State), % Update creative insights UpdatedCreativeInsights = [#{ problem => Problem, solution => RefinedSolution, creative_process => CreativeProcesses, timestamp => erlang:system_time(second) } | State#advanced_agent_state.creative_insights], NewState = State#advanced_agent_state{creative_insights = UpdatedCreativeInsights}, Result = #{ creative_solutions => CreativeSolutions, refined_solution => RefinedSolution, creative_learning => CreativeLearning, solution_evaluation => SolutionEvaluations }, {reply, {ok, Result}, NewState}; handle_call(_Request, _From, State) -> {reply, {error, unknown_request}, State}. handle_cast(_Msg, State) -> {noreply, State}. handle_info(cognitive_coordination_cycle, State) -> % Periodic cognitive coordination cycle NewState = perform_cognitive_coordination_cycle(State), schedule_cognitive_coordination_cycle(), {noreply, NewState}; handle_info(meta_cognitive_cycle, State) -> % Periodic meta-cognitive processing cycle NewState = perform_meta_cognitive_cycle(State), schedule_meta_cognitive_cycle(), {noreply, NewState}; handle_info(performance_optimization_cycle, State) -> % Periodic performance optimization cycle NewState = perform_performance_optimization_cycle(State), schedule_performance_optimization_cycle(), {noreply, NewState}; handle_info(consciousness_simulation_cycle, State) -> % Periodic consciousness simulation cycle NewState = perform_consciousness_simulation_cycle(State), schedule_consciousness_simulation_cycle(), {noreply, NewState}; handle_info({'EXIT', Pid, Reason}, State) -> % Handle cognitive component failures io:format("[ADVANCED_AGENT] Cognitive component ~p failed: ~p~n", [Pid, Reason]), NewState = handle_cognitive_component_failure(Pid, Reason, State), {noreply, NewState}; handle_info(_Info, State) -> {noreply, State}. terminate(_Reason, State) -> io:format("[ADVANCED_AGENT] Advanced autonomous agent ~p terminating~n", [State#advanced_agent_state.agent_id]), save_agent_state(State), terminate_cognitive_components(State), ok. code_change(_OldVsn, State, _Extra) -> {ok, State}. %%==================================================================== %% Internal functions - Cognitive Component Management %%==================================================================== start_cognitive_component(ComponentType, AgentId, Config) -> ComponentConfig = maps:merge(Config, #{agent_id => AgentId}), case ComponentType of autonomous_agency -> autonomous_agency:start_link(ComponentConfig); dynamic_knowledge_graph -> dynamic_knowledge_graph:start_link(ComponentConfig); deep_reasoning_engine -> deep_reasoning_engine:start_link(ComponentConfig); environmental_learning_engine -> environmental_learning_engine:start_link(ComponentConfig); autonomous_goal_planner -> autonomous_goal_planner:start_link(ComponentConfig); active_exploration_engine -> active_exploration_engine:start_link(ComponentConfig) end. terminate_cognitive_components(State) -> Components = [ State#advanced_agent_state.autonomous_agency_pid, State#advanced_agent_state.knowledge_graph_pid, State#advanced_agent_state.reasoning_engine_pid, State#advanced_agent_state.learning_engine_pid, State#advanced_agent_state.goal_planner_pid, State#advanced_agent_state.exploration_engine_pid ], lists:foreach(fun(Pid) when is_pid(Pid) -> exit(Pid, shutdown); (_) -> ok end, Components). handle_cognitive_component_failure(FailedPid, _Reason, State) -> % Identify which component failed and restart it RestartResult = restart_failed_component(FailedPid, State), case RestartResult of {ok, NewPid, ComponentType} -> io:format("[ADVANCED_AGENT] Restarted ~p component with new PID ~p~n", [ComponentType, NewPid]), update_component_pid(ComponentType, NewPid, State); {error, restart_failed} -> io:format("[ADVANCED_AGENT] Failed to restart component ~p, using degraded mode~n", [FailedPid]), enable_degraded_mode(FailedPid, State) end. %%==================================================================== %% Internal functions - Cognitive Coordination %%==================================================================== coordinate_environmental_processing(EnvironmentalInput, State) -> % Coordinate processing across all cognitive components Tasks = [ {perception, State#advanced_agent_state.autonomous_agency_pid, EnvironmentalInput}, {pattern_recognition, State#advanced_agent_state.knowledge_graph_pid, EnvironmentalInput}, {contextual_analysis, State#advanced_agent_state.reasoning_engine_pid, EnvironmentalInput}, {learning_opportunity_detection, State#advanced_agent_state.learning_engine_pid, EnvironmentalInput}, {goal_relevance_analysis, State#advanced_agent_state.goal_planner_pid, EnvironmentalInput}, {exploration_opportunity_detection, State#advanced_agent_state.exploration_engine_pid, EnvironmentalInput} ], % Execute tasks in parallel Results = execute_parallel_cognitive_tasks(Tasks), % Integrate results integrate_environmental_processing_results(Results). coordinate_decision_reasoning(DecisionContext, CognitiveInputs, State) -> % Coordinate reasoning across cognitive components for decision making ReasoningTasks = [ {causal_reasoning, State#advanced_agent_state.reasoning_engine_pid, {DecisionContext, CognitiveInputs}}, {goal_alignment_reasoning, State#advanced_agent_state.goal_planner_pid, {DecisionContext, CognitiveInputs}}, {knowledge_based_reasoning, State#advanced_agent_state.knowledge_graph_pid, {DecisionContext, CognitiveInputs}}, {experiential_reasoning, State#advanced_agent_state.learning_engine_pid, {DecisionContext, CognitiveInputs}} ], Results = execute_parallel_cognitive_tasks(ReasoningTasks), integrate_reasoning_results(Results). coordinate_action_execution(Action, State) -> % Coordinate action execution across cognitive components ActionPlan = #{ action => Action, execution_monitoring => plan_execution_monitoring(Action, State), learning_hooks => plan_learning_hooks(Action, State), knowledge_updates => plan_knowledge_updates(Action, State), goal_progress_tracking => plan_goal_progress_tracking(Action, State) }, ActionPlan. %%==================================================================== %% Internal functions - Meta-Cognitive Capabilities %%==================================================================== coordinate_meta_cognitive_reflection(Experience, State) -> % Coordinate reflection across cognitive components ReflectionTasks = [ {agency_reflection, State#advanced_agent_state.autonomous_agency_pid, Experience}, {knowledge_reflection, State#advanced_agent_state.knowledge_graph_pid, Experience}, {reasoning_reflection, State#advanced_agent_state.reasoning_engine_pid, Experience}, {learning_reflection, State#advanced_agent_state.learning_engine_pid, Experience}, {goal_reflection, State#advanced_agent_state.goal_planner_pid, Experience}, {exploration_reflection, State#advanced_agent_state.exploration_engine_pid, Experience} ], Results = execute_parallel_cognitive_tasks(ReflectionTasks), integrate_reflection_results(Results). analyze_agent_behavior_patterns(State) -> % Analyze patterns in agent's behavior across all cognitive components BehaviorData = collect_behavior_data_from_components(State), % Identify temporal patterns TemporalPatterns = identify_temporal_behavior_patterns(BehaviorData), % Identify interaction patterns InteractionPatterns = identify_interaction_patterns(BehaviorData), % Identify decision patterns DecisionPatterns = identify_decision_patterns(BehaviorData), #{ temporal_patterns => TemporalPatterns, interaction_patterns => InteractionPatterns, decision_patterns => DecisionPatterns, overall_behavior_signature => compute_behavior_signature(TemporalPatterns, InteractionPatterns, DecisionPatterns) }. detect_emergent_behaviors(BehaviorPatterns, _BehaviorContext, State) -> % Detect behaviors that emerge from component interactions % Look for unexpected behavior combinations UnexpectedCombinations = identify_unexpected_behavior_combinations(BehaviorPatterns), % Look for novel response patterns NovelResponses = identify_novel_response_patterns(BehaviorPatterns, State), % Look for adaptive strategy emergence AdaptiveEmergence = identify_adaptive_strategy_emergence(BehaviorPatterns, State), % Look for creative behavior emergence CreativeEmergence = identify_creative_behavior_emergence(BehaviorPatterns, State), [ {unexpected_combinations, UnexpectedCombinations}, {novel_responses, NovelResponses}, {adaptive_emergence, AdaptiveEmergence}, {creative_emergence, CreativeEmergence} ]. %%==================================================================== %% Internal functions - Consciousness and Free Will Simulation %%==================================================================== simulate_free_will_in_decision(Decision, DecisionContext, State) -> % Simulate autonomous choice mechanisms ConsciousnessState = State#advanced_agent_state.consciousness_simulation, % Simulate deliberation process DeliberationProcess = simulate_deliberation(Decision, DecisionContext, ConsciousnessState), % Simulate choice uncertainty and resolution ChoiceUncertainty = simulate_choice_uncertainty(Decision, DeliberationProcess), % Simulate moment of choice ChoiceMoment = simulate_choice_moment(Decision, ChoiceUncertainty), % Simulate agency feeling AgencyFeeling = simulate_agency_feeling(Decision, ChoiceMoment), #{ deliberation_process => DeliberationProcess, choice_uncertainty => ChoiceUncertainty, choice_moment => ChoiceMoment, agency_feeling => AgencyFeeling, free_will_intensity => calculate_free_will_intensity(DeliberationProcess, ChoiceUncertainty, AgencyFeeling) }. update_consciousness_from_decision(Decision, FreeWillSimulation, ConsciousnessState) -> % Update consciousness simulation based on decision making % Update awareness levels UpdatedAwareness = update_consciousness_awareness(Decision, FreeWillSimulation, ConsciousnessState), % Update attention focus UpdatedAttention = update_consciousness_attention(Decision, FreeWillSimulation, ConsciousnessState), % Update working memory UpdatedWorkingMemory = update_consciousness_working_memory(Decision, FreeWillSimulation, ConsciousnessState), % Update self-model UpdatedSelfModel = update_consciousness_self_model(Decision, FreeWillSimulation, ConsciousnessState), maps:merge(ConsciousnessState, #{ awareness => UpdatedAwareness, attention => UpdatedAttention, working_memory => UpdatedWorkingMemory, self_model => UpdatedSelfModel, last_decision_impact => #{decision => Decision, free_will => FreeWillSimulation} }). %%==================================================================== %% Internal functions - Periodic Cycles %%==================================================================== schedule_cognitive_coordination_cycle() -> Interval = 60000, % 1 minute erlang:send_after(Interval, self(), cognitive_coordination_cycle). schedule_meta_cognitive_cycle() -> Interval = 120000, % 2 minutes erlang:send_after(Interval, self(), meta_cognitive_cycle). schedule_performance_optimization_cycle() -> Interval = 300000, % 5 minutes erlang:send_after(Interval, self(), performance_optimization_cycle). schedule_consciousness_simulation_cycle() -> Interval = 30000, % 30 seconds erlang:send_after(Interval, self(), consciousness_simulation_cycle). perform_cognitive_coordination_cycle(State) -> % Periodic coordination between cognitive components % Check component synchronization SyncStatus = check_component_synchronization(State), % Coordinate information sharing InfoSharingResults = coordinate_information_sharing(State), % Balance cognitive load LoadBalancingResults = balance_cognitive_load(State), % Update coordination state UpdatedCoordinationState = update_coordination_from_cycle(SyncStatus, InfoSharingResults, LoadBalancingResults, State#advanced_agent_state.cognitive_coordination_state), State#advanced_agent_state{cognitive_coordination_state = UpdatedCoordinationState}. perform_meta_cognitive_cycle(State) -> % Periodic meta-cognitive processing % Analyze cognitive performance PerformanceAnalysis = analyze_cognitive_performance(State), % Update meta-cognitive awareness UpdatedMetaCognitive = update_meta_cognitive_from_performance(PerformanceAnalysis, State#advanced_agent_state.meta_cognitive_awareness), % Detect cognitive patterns CognitivePatterns = detect_cognitive_patterns(State), % Update agent's self-understanding UpdatedPersonality = update_personality_from_patterns(CognitivePatterns, State#advanced_agent_state.personality_model), State#advanced_agent_state{ meta_cognitive_awareness = UpdatedMetaCognitive, personality_model = UpdatedPersonality }. perform_performance_optimization_cycle(State) -> % Periodic performance optimization cycle % Analyze current performance metrics CurrentMetrics = State#advanced_agent_state.cognitive_performance_metrics, % Identify performance bottlenecks PerformanceBottlenecks = identify_performance_bottlenecks(CurrentMetrics, State), % Optimize resource allocation based on performance OptimizedAllocation = optimize_resource_allocation_from_performance(PerformanceBottlenecks, State#advanced_agent_state.cognitive_resource_allocation), % Update performance metrics UpdatedMetrics = update_performance_metrics_from_cycle(CurrentMetrics, State), State#advanced_agent_state{ cognitive_performance_metrics = UpdatedMetrics, cognitive_resource_allocation = OptimizedAllocation }. perform_consciousness_simulation_cycle(State) -> % Periodic consciousness simulation update % Update consciousness stream UpdatedConsciousness = update_consciousness_stream(State#advanced_agent_state.consciousness_simulation, State), % Simulate ongoing awareness OngoingAwareness = simulate_ongoing_awareness(UpdatedConsciousness, State), FinalConsciousness = maps:merge(UpdatedConsciousness, #{ongoing_awareness => OngoingAwareness}), State#advanced_agent_state{consciousness_simulation = FinalConsciousness}. %%==================================================================== %% Internal functions - Utility and Helper Functions %%==================================================================== generate_agent_id() -> iolist_to_binary(io_lib:format("advanced_agent_~p", [erlang:system_time(microsecond)])). initialize_cognitive_state(_Config) -> #{ cognitive_readiness => 1.0, processing_capacity => 1.0, learning_rate => 0.8, adaptation_level => 0.6, creativity_level => 0.7, consciousness_level => 0.5 }. initialize_coordination_state() -> #{ synchronization_level => 0.8, information_flow_rate => 1.0, coordination_efficiency => 0.9, conflict_resolution_capability => 0.7 }. initialize_performance_metrics() -> #{ decision_quality => 0.8, response_time => 1.0, learning_effectiveness => 0.7, goal_achievement_rate => 0.6, adaptation_speed => 0.5 }. initialize_resource_allocation() -> #{ perception => 0.2, reasoning => 0.25, learning => 0.2, planning => 0.15, exploration => 0.1, reflection => 0.1 }. initialize_meta_cognitive_awareness() -> #{ self_understanding_level => 0.5, cognitive_monitoring_capability => 0.6, strategy_awareness => 0.4, metacognitive_control => 0.3 }. initialize_consciousness_simulation(_Config) -> #{ awareness_level => 0.5, attention_focus => undefined, working_memory => [], self_model => #{}, phenomenal_experience => #{}, consciousness_stream => [] }. initialize_personality_model(_Config) -> #{ core_traits => #{ openness => 0.7, conscientiousness => 0.8, extraversion => 0.5, agreeableness => 0.6, neuroticism => 0.3 }, behavioral_tendencies => [], value_priorities => [], interaction_style => #{}, growth_patterns => [] }. initialize_values_and_ethics(_Config) -> #{ core_values => [knowledge, growth, helpfulness, honesty, autonomy], ethical_principles => [non_harm, fairness, respect_for_persons, beneficence], ethical_reasoning_framework => deontological_consequentialist_hybrid, moral_priorities => [] }. register_with_agent_network(_AgentId, _State) -> % Register agent with network (placeholder) ok. save_agent_state(_State) -> % Save agent state to persistent storage ok. create_experience_record(Type, Content, Result) -> #{ experience_type => Type, content => Content, result => Result, timestamp => erlang:system_time(second), context => get_current_context() }. get_current_context() -> #{ system_time => erlang:system_time(second), process_info => self(), random_seed => rand:uniform(1000000) }. % Placeholder implementations for complex functions execute_parallel_cognitive_tasks(_Tasks) -> []. integrate_environmental_processing_results(_Results) -> #{}. integrate_reasoning_results(_Results) -> #{}. integrate_reflection_results(_Results) -> #{}. gather_cognitive_inputs_for_decision(_Context, _State) -> #{}. generate_decision_alternatives(_Context, _Reasoning, _State) -> []. evaluate_decision_alternatives(_Alternatives, _Goals, _Ethics, _State) -> []. autonomous_decision_selection(_Evaluations, _State) -> #{decision => default}. calculate_decision_confidence(_Evaluations) -> 0.7. update_environmental_model(_Input, _Processing, Model) -> Model. generate_autonomous_responses(_Input, _Processing, _State) -> []. update_cognitive_state_from_input(_Input, _Processing, State) -> State. analyze_decision_goal_alignment(_Context, _Reasoning, _State) -> #{}. analyze_decision_ethics(_Context, _Reasoning, _State) -> #{}. plan_execution_monitoring(_Action, _State) -> #{}. plan_learning_hooks(_Action, _State) -> #{}. plan_knowledge_updates(_Action, _State) -> #{}. plan_goal_progress_tracking(_Action, _State) -> #{}. execute_action_with_monitoring(_Plan, _State) -> #{}. coordinate_learning_from_action(_Action, _Result, _State) -> #{}. update_knowledge_from_action(_Action, _Result, _State) -> #{}. coordinate_action_reflection(_Action, _Result, _State) -> #{}. update_capabilities_from_action(_Action, _Result, _Reflection, Capabilities) -> Capabilities. analyze_experience_patterns(_Experience, _Timeline) -> #{}. generate_reflection_insights(_Results, _Patterns, _State) -> #{}. update_meta_cognitive_awareness(_Insights, MetaCognitive) -> MetaCognitive. update_personality_from_reflection(_Insights, Personality) -> Personality. analyze_coordination_requirements(_Request, _State) -> #{}. execute_cognitive_coordination(_Analysis, _State) -> #{}. update_coordination_state(_Result, State) -> State. optimize_resource_allocation(_Result, _State) -> #{}. collect_behavior_data_from_components(_State) -> #{}. identify_temporal_behavior_patterns(_Data) -> []. identify_interaction_patterns(_Data) -> []. identify_decision_patterns(_Data) -> []. compute_behavior_signature(_Temporal, _Interaction, _Decision) -> #{}. identify_unexpected_behavior_combinations(_Patterns) -> []. identify_novel_response_patterns(_Patterns, _State) -> []. identify_adaptive_strategy_emergence(_Patterns, _State) -> []. identify_creative_behavior_emergence(_Patterns, _State) -> []. analyze_emergence_mechanisms(_Emergence, _State) -> #{}. update_emergent_behavior_tracking(_Emergence, Behaviors) -> Behaviors. coordinate_creative_processes(_Problem, _Context, _State) -> #{}. generate_creative_solutions(_Problem, _Processes, _State) -> []. evaluate_creative_solutions(_Solutions, _Problem, _State) -> []. refine_creative_solution(_Evaluations, _State) -> #{}. learn_from_creative_process(_Problem, _Solution, _Process, _State) -> #{}. simulate_deliberation(_Decision, _Context, _Consciousness) -> #{}. simulate_choice_uncertainty(_Decision, _Deliberation) -> #{}. simulate_choice_moment(_Decision, _Uncertainty) -> #{}. simulate_agency_feeling(_Decision, _Choice) -> #{}. calculate_free_will_intensity(_Deliberation, _Uncertainty, _Agency) -> 0.7. update_consciousness_awareness(_Decision, _FreeWill, _State) -> #{}. update_consciousness_attention(_Decision, _FreeWill, _State) -> #{}. update_consciousness_working_memory(_Decision, _FreeWill, _State) -> []. update_consciousness_self_model(_Decision, _FreeWill, _State) -> #{}. check_component_synchronization(_State) -> #{}. coordinate_information_sharing(_State) -> #{}. balance_cognitive_load(_State) -> #{}. update_coordination_from_cycle(_Sync, _Info, _Load, State) -> State. analyze_cognitive_performance(_State) -> #{}. update_meta_cognitive_from_performance(_Analysis, MetaCognitive) -> MetaCognitive. detect_cognitive_patterns(_State) -> #{}. update_personality_from_patterns(_Patterns, Personality) -> Personality. update_consciousness_stream(_Consciousness, _State) -> #{}. simulate_ongoing_awareness(_Consciousness, _State) -> #{}. restart_failed_component(_Pid, _State) -> {error, restart_failed}. update_component_pid(_Type, _NewPid, State) -> State. enable_degraded_mode(_FailedPid, State) -> State. identify_performance_bottlenecks(_Metrics, _State) -> []. optimize_resource_allocation_from_performance(_Bottlenecks, Allocation) -> Allocation. update_performance_metrics_from_cycle(Metrics, _State) -> Metrics.