agents

-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.