agents

-module(autonomous_agency).
-behaviour(gen_server).

%% Autonomous Agency System - Active Intelligence with Environmental Learning
%% This module implements a sophisticated autonomous agent capable of:
%% - Environmental perception and model building
%% - Autonomous goal formation and pursuit
%% - Active exploration and knowledge discovery
%% - Deep reasoning and meta-cognitive reflection
%% - Dynamic adaptation and learning

-export([start_link/1,
         % Core agency functions
         perceive_environment/0, form_autonomous_goals/1, execute_exploration/1,
         reflect_on_experience/1, adapt_behavior/2,
         % Knowledge and reasoning
         build_mental_model/1, reason_about_causality/2, generate_hypotheses/1,
         test_hypothesis/2, update_beliefs/2,
         % Meta-cognitive functions
         introspect/0, evaluate_cognitive_state/0, plan_cognitive_strategy/1,
         % Environmental interaction
         explore_domain/1, discover_patterns/1, form_abstractions/1,
         % Autonomy and agency
         set_autonomous_mode/1, get_agency_status/0, trigger_curiosity/1]).

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

%% Core data structures for autonomous agency
-record(cognitive_state, {
    consciousness_level = 0.0,          % Current level of self-awareness (0-1)
    attention_focus = undefined,         % Current focus of attention
    cognitive_load = 0.0,               % Current cognitive processing load
    meta_cognitive_awareness = #{},      % Self-knowledge about cognitive processes
    emotional_state = neutral,           % Current emotional/motivational state
    energy_level = 1.0,                 % Available cognitive energy (0-1)
    learning_mode = active              % Current learning strategy
}).

-record(environmental_model, {
    perceived_entities = #{},           % Map of perceived objects/entities
    spatial_relationships = #{},        % Spatial relations between entities
    temporal_patterns = [],             % Observed temporal sequences
    causal_relationships = #{},         % Inferred causal connections
    uncertainty_map = #{},              % Uncertainty about various aspects
    exploration_frontiers = [],         % Areas identified for exploration
    anomaly_detections = []             % Detected anomalies or unexpected patterns
}).

-record(autonomous_goals, {
    survival_goals = [],                % Basic survival and maintenance goals
    exploration_goals = [],             % Curiosity-driven exploration goals
    knowledge_goals = [],               % Learning and understanding goals
    optimization_goals = [],            % Improvement and efficiency goals
    creative_goals = [],                % Novel creation and synthesis goals
    meta_goals = [],                    % Goals about goals (meta-level)
    active_pursuits = #{},              % Currently active goal pursuits
    goal_hierarchy = []                 % Hierarchical goal structure
}).

-record(agency_state, {
    agent_id,                           % Unique agent identifier
    cognitive_state = #cognitive_state{}, % Current cognitive state
    environmental_model = #environmental_model{}, % Model of environment
    autonomous_goals = #autonomous_goals{}, % Current goal structure
    knowledge_graph,                    % Dynamic knowledge representation
    exploration_strategy = curiosity_driven, % Current exploration approach
    learning_history = [],              % History of learning episodes
    reflection_insights = [],           % Insights from meta-cognitive reflection
    behavioral_patterns = #{},          % Learned behavioral patterns
    autonomy_level = 0.5,              % Level of autonomous operation (0-1)
    last_reflection = undefined,        % Timestamp of last meta-cognitive reflection
    active_explorations = #{},          % Currently active exploration processes
    environmental_sensors = []          % Available environmental sensing capabilities
}).

%%====================================================================
%% API functions
%%====================================================================

start_link(Config) ->
    AgentId = maps:get(agent_id, Config, generate_agent_id()),
    io:format("[AGENCY] Starting autonomous agent ~p with config: ~p~n", [AgentId, Config]),
    gen_server:start_link({local, ?MODULE}, ?MODULE, [AgentId, Config], []).

%% Core agency functions
perceive_environment() ->
    gen_server:call(?MODULE, perceive_environment).

form_autonomous_goals(Context) ->
    gen_server:call(?MODULE, {form_autonomous_goals, Context}).

execute_exploration(Strategy) ->
    gen_server:call(?MODULE, {execute_exploration, Strategy}).

reflect_on_experience(Experience) ->
    gen_server:call(?MODULE, {reflect_on_experience, Experience}).

adapt_behavior(Feedback, Context) ->
    gen_server:call(?MODULE, {adapt_behavior, Feedback, Context}).

%% Knowledge and reasoning functions
build_mental_model(Observations) ->
    gen_server:call(?MODULE, {build_mental_model, Observations}).

reason_about_causality(Event1, Event2) ->
    gen_server:call(?MODULE, {reason_about_causality, Event1, Event2}).

generate_hypotheses(Domain) ->
    gen_server:call(?MODULE, {generate_hypotheses, Domain}).

test_hypothesis(Hypothesis, TestStrategy) ->
    gen_server:call(?MODULE, {test_hypothesis, Hypothesis, TestStrategy}).

update_beliefs(Evidence, ConfidenceLevel) ->
    gen_server:call(?MODULE, {update_beliefs, Evidence, ConfidenceLevel}).

%% Meta-cognitive functions
introspect() ->
    gen_server:call(?MODULE, introspect).

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

plan_cognitive_strategy(Objective) ->
    gen_server:call(?MODULE, {plan_cognitive_strategy, Objective}).

%% Environmental interaction
explore_domain(Domain) ->
    gen_server:call(?MODULE, {explore_domain, Domain}).

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

form_abstractions(ConcreteExamples) ->
    gen_server:call(?MODULE, {form_abstractions, ConcreteExamples}).

%% Autonomy and agency control
set_autonomous_mode(Level) ->
    gen_server:call(?MODULE, {set_autonomous_mode, Level}).

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

trigger_curiosity(Stimulus) ->
    gen_server:cast(?MODULE, {trigger_curiosity, Stimulus}).

%%====================================================================
%% gen_server callbacks
%%====================================================================

init([AgentId, Config]) ->
    process_flag(trap_exit, true),
    
    io:format("[AGENCY] Initializing autonomous agent ~p~n", [AgentId]),
    
    % Initialize knowledge graph
    {ok, KnowledgeGraph} = dynamic_knowledge_graph:start_link(#{agent_id => AgentId}),
    
    % Set up environmental sensors
    Sensors = maps:get(sensors, Config, [system_monitor, file_system, network_monitor]),
    
    State = #agency_state{
        agent_id = AgentId,
        knowledge_graph = KnowledgeGraph,
        environmental_sensors = Sensors,
        autonomy_level = maps:get(autonomy_level, Config, 0.5)
    },
    
    % Start autonomous processes
    schedule_autonomous_cycle(),
    schedule_reflection_cycle(),
    schedule_exploration_cycle(),
    
    io:format("[AGENCY] Agent ~p initialized with autonomy level ~p~n", 
              [AgentId, State#agency_state.autonomy_level]),
    
    {ok, State}.

handle_call(perceive_environment, _From, State) ->
    io:format("[AGENCY] Perceiving environment...~n"),
    
    % Multi-modal environmental perception
    Perceptions = perform_environmental_perception(State),
    
    % Update environmental model
    NewEnvironmentalModel = update_environmental_model(Perceptions, 
                                                      State#agency_state.environmental_model),
    
    % Update cognitive state based on perceptions
    NewCognitiveState = update_cognitive_state_from_perception(Perceptions, 
                                                              State#agency_state.cognitive_state),
    
    NewState = State#agency_state{
        environmental_model = NewEnvironmentalModel,
        cognitive_state = NewCognitiveState
    },
    
    {reply, {ok, Perceptions}, NewState};

handle_call({form_autonomous_goals, Context}, _From, State) ->
    io:format("[AGENCY] Forming autonomous goals for context: ~p~n", [Context]),
    
    % Analyze current state and context
    CurrentGoals = State#agency_state.autonomous_goals,
    CognitiveState = State#agency_state.cognitive_state,
    EnvironmentalModel = State#agency_state.environmental_model,
    
    % Generate new autonomous goals based on:
    % 1. Current needs and drives
    % 2. Environmental opportunities
    % 3. Knowledge gaps and curiosity
    % 4. Meta-cognitive objectives
    NewGoals = generate_autonomous_goals(Context, CurrentGoals, CognitiveState, EnvironmentalModel),
    
    % Prioritize and organize goals
    OrganizedGoals = organize_goal_hierarchy(NewGoals, State),
    
    NewState = State#agency_state{autonomous_goals = OrganizedGoals},
    
    {reply, {ok, OrganizedGoals}, NewState};

handle_call({execute_exploration, Strategy}, _From, State) ->
    io:format("[AGENCY] Executing exploration with strategy: ~p~n", [Strategy]),
    
    % Plan exploration based on current knowledge and gaps
    ExplorationPlan = plan_exploration(Strategy, State),
    
    % Execute exploration
    ExplorationResults = execute_exploration_plan(ExplorationPlan, State),
    
    % Update knowledge graph with discoveries
    update_knowledge_graph_from_exploration(ExplorationResults, State#agency_state.knowledge_graph),
    
    % Update environmental model
    NewEnvironmentalModel = integrate_exploration_results(ExplorationResults, 
                                                         State#agency_state.environmental_model),
    
    NewState = State#agency_state{environmental_model = NewEnvironmentalModel},
    
    {reply, {ok, ExplorationResults}, NewState};

handle_call({reflect_on_experience, Experience}, _From, State) ->
    io:format("[AGENCY] Reflecting on experience: ~p~n", [Experience]),
    
    % Deep meta-cognitive reflection
    ReflectionInsights = perform_meta_cognitive_reflection(Experience, State),
    
    % Update cognitive state and self-model
    NewCognitiveState = update_cognitive_state_from_reflection(ReflectionInsights, 
                                                              State#agency_state.cognitive_state),
    
    % Update behavioral patterns
    NewBehavioralPatterns = update_behavioral_patterns(ReflectionInsights, 
                                                      State#agency_state.behavioral_patterns),
    
    NewState = State#agency_state{
        cognitive_state = NewCognitiveState,
        behavioral_patterns = NewBehavioralPatterns,
        reflection_insights = [ReflectionInsights | State#agency_state.reflection_insights],
        last_reflection = erlang:system_time(second)
    },
    
    {reply, {ok, ReflectionInsights}, NewState};

handle_call({adapt_behavior, Feedback, Context}, _From, State) ->
    io:format("[AGENCY] Adapting behavior based on feedback: ~p in context: ~p~n", [Feedback, Context]),
    
    % Analyze feedback and determine adaptations
    Adaptations = analyze_feedback_and_adapt(Feedback, Context, State),
    
    % Update behavioral patterns
    NewBehavioralPatterns = apply_behavioral_adaptations(Adaptations, 
                                                        State#agency_state.behavioral_patterns),
    
    % Update learning history
    LearningEpisode = #{
        feedback => Feedback,
        context => Context,
        adaptations => Adaptations,
        timestamp => erlang:system_time(second)
    },
    
    NewState = State#agency_state{
        behavioral_patterns = NewBehavioralPatterns,
        learning_history = [LearningEpisode | State#agency_state.learning_history]
    },
    
    {reply, {ok, Adaptations}, NewState};

handle_call({build_mental_model, Observations}, _From, State) ->
    io:format("[AGENCY] Building mental model from observations...~n"),
    
    % Construct internal representation of observed phenomena
    MentalModel = construct_mental_model(Observations, State),
    
    % Integrate with existing environmental model
    IntegratedModel = integrate_mental_model(MentalModel, State#agency_state.environmental_model),
    
    % Update knowledge graph
    update_knowledge_graph_from_mental_model(MentalModel, State#agency_state.knowledge_graph),
    
    NewState = State#agency_state{environmental_model = IntegratedModel},
    
    {reply, {ok, MentalModel}, NewState};

handle_call({reason_about_causality, Event1, Event2}, _From, State) ->
    io:format("[AGENCY] Reasoning about causality between ~p and ~p~n", [Event1, Event2]),
    
    % Causal reasoning and inference
    CausalAnalysis = perform_causal_reasoning(Event1, Event2, State),
    
    % Update causal relationships in environmental model
    NewCausalRelationships = update_causal_relationships(CausalAnalysis, 
                                                        State#agency_state.environmental_model),
    
    NewEnvironmentalModel = State#agency_state.environmental_model#environmental_model{
        causal_relationships = NewCausalRelationships
    },
    
    NewState = State#agency_state{environmental_model = NewEnvironmentalModel},
    
    {reply, {ok, CausalAnalysis}, NewState};

handle_call({generate_hypotheses, Domain}, _From, State) ->
    io:format("[AGENCY] Generating hypotheses for domain: ~p~n", [Domain]),
    
    % Creative hypothesis generation
    Hypotheses = generate_creative_hypotheses(Domain, State),
    
    % Evaluate hypotheses for testability and relevance
    EvaluatedHypotheses = evaluate_hypotheses(Hypotheses, State),
    
    {reply, {ok, EvaluatedHypotheses}, State};

handle_call({test_hypothesis, Hypothesis, TestStrategy}, _From, State) ->
    io:format("[AGENCY] Testing hypothesis: ~p with strategy: ~p~n", [Hypothesis, TestStrategy]),
    
    % Design and execute hypothesis test
    TestResults = execute_hypothesis_test(Hypothesis, TestStrategy, State),
    
    % Update beliefs based on test results
    _NewBeliefs = update_beliefs_from_test(TestResults, State),
    
    {reply, {ok, TestResults}, State};

handle_call({update_beliefs, Evidence, ConfidenceLevel}, _From, State) ->
    io:format("[AGENCY] Updating beliefs with evidence (confidence: ~p)~n", [ConfidenceLevel]),
    
    % Bayesian belief updating
    UpdatedBeliefs = perform_bayesian_update(Evidence, ConfidenceLevel, State),
    
    % Update knowledge graph with new beliefs
    update_knowledge_graph_beliefs(UpdatedBeliefs, State#agency_state.knowledge_graph),
    
    {reply, {ok, UpdatedBeliefs}, State};

handle_call(introspect, _From, State) ->
    io:format("[AGENCY] Performing introspection...~n"),
    
    % Deep self-examination and awareness
    IntrospectionResults = perform_introspection(State),
    
    % Update meta-cognitive awareness
    NewMetaCognitiveAwareness = update_meta_cognitive_awareness(IntrospectionResults, 
                                                               State#agency_state.cognitive_state),
    
    NewCognitiveState = State#agency_state.cognitive_state#cognitive_state{
        meta_cognitive_awareness = NewMetaCognitiveAwareness,
        consciousness_level = calculate_consciousness_level(IntrospectionResults)
    },
    
    NewState = State#agency_state{cognitive_state = NewCognitiveState},
    
    {reply, {ok, IntrospectionResults}, NewState};

handle_call(evaluate_cognitive_state, _From, State) ->
    io:format("[AGENCY] Evaluating cognitive state...~n"),
    
    CognitiveEvaluation = evaluate_current_cognitive_state(State#agency_state.cognitive_state),
    
    {reply, {ok, CognitiveEvaluation}, State};

handle_call({plan_cognitive_strategy, Objective}, _From, State) ->
    io:format("[AGENCY] Planning cognitive strategy for objective: ~p~n", [Objective]),
    
    % Meta-cognitive planning
    CognitiveStrategy = plan_cognitive_approach(Objective, State),
    
    {reply, {ok, CognitiveStrategy}, State};

handle_call({explore_domain, Domain}, _From, State) ->
    io:format("[AGENCY] Exploring domain: ~p~n", [Domain]),
    
    % Systematic domain exploration
    ExplorationResults = explore_domain_systematically(Domain, State),
    
    % Update knowledge and environmental model
    _NewKnowledge = integrate_domain_knowledge(ExplorationResults, State),
    
    {reply, {ok, ExplorationResults}, State};

handle_call({discover_patterns, Data}, _From, State) ->
    io:format("[AGENCY] Discovering patterns in data...~n"),
    
    % Pattern discovery and abstraction
    DiscoveredPatterns = discover_patterns_in_data(Data, State),
    
    % Add patterns to knowledge graph
    add_patterns_to_knowledge_graph(DiscoveredPatterns, State#agency_state.knowledge_graph),
    
    {reply, {ok, DiscoveredPatterns}, State};

handle_call({form_abstractions, ConcreteExamples}, _From, State) ->
    io:format("[AGENCY] Forming abstractions from concrete examples...~n"),
    
    % Abstraction and concept formation
    Abstractions = form_concept_abstractions(ConcreteExamples, State),
    
    % Add abstractions to knowledge graph
    add_abstractions_to_knowledge_graph(Abstractions, State#agency_state.knowledge_graph),
    
    {reply, {ok, Abstractions}, State};

handle_call({set_autonomous_mode, Level}, _From, State) ->
    io:format("[AGENCY] Setting autonomy level to: ~p~n", [Level]),
    
    ValidatedLevel = max(0.0, min(1.0, Level)),
    NewState = State#agency_state{autonomy_level = ValidatedLevel},
    
    % Adjust autonomous processes based on new level
    adjust_autonomous_processes(ValidatedLevel),
    
    {reply, {ok, ValidatedLevel}, NewState};

handle_call(get_agency_status, _From, State) ->
    Status = #{
        agent_id => State#agency_state.agent_id,
        autonomy_level => State#agency_state.autonomy_level,
        cognitive_state => summarize_cognitive_state(State#agency_state.cognitive_state),
        active_goals => length(State#agency_state.autonomous_goals#autonomous_goals.active_pursuits),
        exploration_frontiers => length(State#agency_state.environmental_model#environmental_model.exploration_frontiers),
        knowledge_graph_size => get_knowledge_graph_size(State#agency_state.knowledge_graph),
        last_reflection => State#agency_state.last_reflection,
        learning_episodes => length(State#agency_state.learning_history)
    },
    
    {reply, Status, State};

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

handle_cast({trigger_curiosity, Stimulus}, State) ->
    io:format("[AGENCY] Curiosity triggered by stimulus: ~p~n", [Stimulus]),
    
    % Generate curiosity-driven exploration goals
    CuriosityGoals = generate_curiosity_goals(Stimulus, State),
    
    % Add to autonomous goals
    CurrentGoals = State#agency_state.autonomous_goals,
    UpdatedGoals = CurrentGoals#autonomous_goals{
        exploration_goals = CuriosityGoals ++ CurrentGoals#autonomous_goals.exploration_goals
    },
    
    NewState = State#agency_state{autonomous_goals = UpdatedGoals},
    
    {noreply, NewState};

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

handle_info(autonomous_cycle, State) ->
    % Autonomous cognitive cycle
    case State#agency_state.autonomy_level > 0.3 of
        true ->
            NewState = execute_autonomous_cycle(State),
            schedule_autonomous_cycle(),
            {noreply, NewState};
        false ->
            schedule_autonomous_cycle(),
            {noreply, State}
    end;

handle_info(reflection_cycle, State) ->
    % Periodic meta-cognitive reflection
    case State#agency_state.autonomy_level > 0.5 of
        true ->
            NewState = execute_reflection_cycle(State),
            schedule_reflection_cycle(),
            {noreply, NewState};
        false ->
            schedule_reflection_cycle(),
            {noreply, State}
    end;

handle_info(exploration_cycle, State) ->
    % Autonomous exploration cycle
    case State#agency_state.autonomy_level > 0.4 of
        true ->
            NewState = execute_exploration_cycle(State),
            schedule_exploration_cycle(),
            {noreply, NewState};
        false ->
            schedule_exploration_cycle(),
            {noreply, State}
    end;

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

terminate(_Reason, State) ->
    io:format("[AGENCY] Agent ~p terminating~n", [State#agency_state.agent_id]),
    % Save state and knowledge before termination
    save_agent_state(State),
    ok.

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

%%====================================================================
%% Internal functions - Environmental Perception
%%====================================================================

perform_environmental_perception(State) ->
    Sensors = State#agency_state.environmental_sensors,
    
    % Multi-modal perception
    SystemPerception = perceive_system_environment(),
    FileSystemPerception = perceive_file_system(),
    NetworkPerception = perceive_network_environment(),
    CodebasePerception = perceive_codebase_structure(),
    
    % Integrate perceptions
    #{
        system => SystemPerception,
        filesystem => FileSystemPerception,
        network => NetworkPerception,
        codebase => CodebasePerception,
        timestamp => erlang:system_time(second),
        sensors_used => Sensors
    }.

perceive_system_environment() ->
    % System-level environmental perception
    #{
        processes => get_system_processes(),
        memory_usage => get_memory_usage(),
        cpu_usage => get_cpu_usage(),
        active_connections => get_active_connections(),
        system_events => get_recent_system_events()
    }.

perceive_file_system() ->
    % File system structure and changes
    #{
        directory_structure => analyze_directory_structure(),
        recent_changes => detect_file_changes(),
        file_types => categorize_file_types(),
        access_patterns => analyze_access_patterns()
    }.

perceive_network_environment() ->
    % Network environment and connectivity
    #{
        active_connections => get_network_connections(),
        traffic_patterns => analyze_network_traffic(),
        available_services => discover_network_services(),
        connectivity_status => assess_connectivity()
    }.

perceive_codebase_structure() ->
    % Codebase and software environment
    #{
        modules => analyze_code_modules(),
        dependencies => map_dependencies(),
        api_endpoints => discover_api_endpoints(),
        database_schemas => analyze_data_structures()
    }.

update_environmental_model(Perceptions, CurrentModel) ->
    % Update environmental model with new perceptions
    NewEntities = extract_entities_from_perceptions(Perceptions),
    NewRelationships = infer_relationships_from_perceptions(Perceptions),
    NewPatterns = detect_temporal_patterns(Perceptions, CurrentModel),
    
    CurrentModel#environmental_model{
        perceived_entities = maps:merge(CurrentModel#environmental_model.perceived_entities, NewEntities),
        spatial_relationships = maps:merge(CurrentModel#environmental_model.spatial_relationships, NewRelationships),
        temporal_patterns = NewPatterns ++ CurrentModel#environmental_model.temporal_patterns,
        exploration_frontiers = identify_exploration_frontiers(Perceptions, CurrentModel)
    }.

%%====================================================================
%% Internal functions - Autonomous Goal Formation
%%====================================================================

generate_autonomous_goals(Context, CurrentGoals, CognitiveState, EnvironmentalModel) ->
    % Generate goals based on multiple drives and motivations
    
    % Survival and maintenance goals
    SurvivalGoals = generate_survival_goals(CognitiveState, EnvironmentalModel),
    
    % Curiosity and exploration goals
    ExplorationGoals = generate_exploration_goals(EnvironmentalModel),
    
    % Knowledge acquisition goals
    KnowledgeGoals = generate_knowledge_goals(Context, EnvironmentalModel),
    
    % Optimization and improvement goals
    OptimizationGoals = generate_optimization_goals(CognitiveState),
    
    % Creative and synthetic goals
    CreativeGoals = generate_creative_goals(Context, EnvironmentalModel),
    
    % Meta-cognitive goals
    MetaGoals = generate_meta_goals(CognitiveState),
    
    CurrentGoals#autonomous_goals{
        survival_goals = SurvivalGoals,
        exploration_goals = ExplorationGoals,
        knowledge_goals = KnowledgeGoals,
        optimization_goals = OptimizationGoals,
        creative_goals = CreativeGoals,
        meta_goals = MetaGoals
    }.

generate_survival_goals(CognitiveState, EnvironmentalModel) ->
    % Goals related to agent survival and basic maintenance
    Goals = [],
    
    % Resource management
    Goals1 = case CognitiveState#cognitive_state.energy_level < 0.3 of
        true -> [{manage_cognitive_resources, high_priority} | Goals];
        false -> Goals
    end,
    
    % Error detection and recovery
    Goals2 = case detect_system_anomalies(EnvironmentalModel) of
        [] -> Goals1;
        Anomalies -> [{investigate_anomalies, Anomalies, medium_priority} | Goals1]
    end,
    
    % Self-monitoring and health checks
    [{monitor_agent_health, low_priority} | Goals2].

generate_exploration_goals(EnvironmentalModel) ->
    % Curiosity-driven exploration goals
    Frontiers = EnvironmentalModel#environmental_model.exploration_frontiers,
    
    % Generate goals for each frontier
    ExplorationGoals = [
        {explore_frontier, Frontier, calculate_exploration_priority(Frontier)}
        || Frontier <- Frontiers
    ],
    
    % Add novel pattern discovery goals
    PatternGoals = [{discover_new_patterns, medium_priority}],
    
    ExplorationGoals ++ PatternGoals.

generate_knowledge_goals(Context, EnvironmentalModel) ->
    % Goals related to learning and understanding
    
    % Identify knowledge gaps
    KnowledgeGaps = identify_knowledge_gaps(EnvironmentalModel),
    
    % Generate learning goals
    LearningGoals = [
        {learn_about, Gap, calculate_learning_priority(Gap)}
        || Gap <- KnowledgeGaps
    ],
    
    % Add conceptual understanding goals
    ConceptualGoals = [{deepen_conceptual_understanding, Context, medium_priority}],
    
    LearningGoals ++ ConceptualGoals.

organize_goal_hierarchy(Goals, State) ->
    % Organize goals into a hierarchical structure
    
    % Sort goals by priority
    AllGoals = lists:flatten([
        Goals#autonomous_goals.survival_goals,
        Goals#autonomous_goals.exploration_goals,
        Goals#autonomous_goals.knowledge_goals,
        Goals#autonomous_goals.optimization_goals,
        Goals#autonomous_goals.creative_goals,
        Goals#autonomous_goals.meta_goals
    ]),
    
    SortedGoals = lists:sort(fun compare_goal_priority/2, AllGoals),
    
    % Create hierarchy
    Hierarchy = create_goal_hierarchy(SortedGoals),
    
    Goals#autonomous_goals{
        goal_hierarchy = Hierarchy,
        active_pursuits = select_active_pursuits(SortedGoals, State#agency_state.autonomy_level)
    }.

%%====================================================================
%% Internal functions - Meta-Cognitive Reflection
%%====================================================================

perform_meta_cognitive_reflection(Experience, State) ->
    % Deep reflection on experience and learning
    
    % Analyze what happened
    ExperienceAnalysis = analyze_experience(Experience, State),
    
    % Reflect on cognitive processes used
    CognitiveProcessAnalysis = analyze_cognitive_processes(Experience, State),
    
    % Identify lessons learned
    LessonsLearned = extract_lessons_learned(ExperienceAnalysis, CognitiveProcessAnalysis),
    
    % Generate insights about self and environment
    SelfInsights = generate_self_insights(Experience, State),
    EnvironmentalInsights = generate_environmental_insights(Experience, State),
    
    % Meta-learning: learning about learning
    MetaLearningInsights = perform_meta_learning_analysis(Experience, State),
    
    #{
        experience_analysis => ExperienceAnalysis,
        cognitive_process_analysis => CognitiveProcessAnalysis,
        lessons_learned => LessonsLearned,
        self_insights => SelfInsights,
        environmental_insights => EnvironmentalInsights,
        meta_learning_insights => MetaLearningInsights,
        reflection_timestamp => erlang:system_time(second),
        reflection_depth => calculate_reflection_depth(Experience, State)
    }.

perform_introspection(State) ->
    % Deep self-examination and self-awareness
    
    % Examine current cognitive state
    CognitiveStateAnalysis = analyze_current_cognitive_state(State#agency_state.cognitive_state),
    
    % Examine goal structure and motivations
    GoalAnalysis = analyze_goal_structure(State#agency_state.autonomous_goals),
    
    % Examine behavioral patterns
    BehavioralAnalysis = analyze_behavioral_patterns(State#agency_state.behavioral_patterns),
    
    % Examine knowledge and beliefs
    KnowledgeAnalysis = analyze_knowledge_state(State#agency_state.knowledge_graph),
    
    % Self-model construction and updating
    SelfModel = construct_self_model(CognitiveStateAnalysis, GoalAnalysis, BehavioralAnalysis),
    
    #{
        cognitive_state_analysis => CognitiveStateAnalysis,
        goal_analysis => GoalAnalysis,
        behavioral_analysis => BehavioralAnalysis,
        knowledge_analysis => KnowledgeAnalysis,
        self_model => SelfModel,
        introspection_timestamp => erlang:system_time(second)
    }.

%%====================================================================
%% Internal functions - Autonomous Cycles
%%====================================================================

execute_autonomous_cycle(State) ->
    % Main autonomous cognitive cycle
    
    % 1. Perceive environment
    Perceptions = perform_environmental_perception(State),
    
    % 2. Update models
    NewEnvironmentalModel = update_environmental_model(Perceptions, State#agency_state.environmental_model),
    
    % 3. Evaluate current goals
    GoalEvaluation = evaluate_current_goals(State#agency_state.autonomous_goals, NewEnvironmentalModel),
    
    % 4. Make decisions about next actions
    Decisions = make_autonomous_decisions(GoalEvaluation, State),
    
    % 5. Execute decisions
    ExecutionResults = execute_autonomous_decisions(Decisions, State),
    
    % 6. Learn from results
    LearningResults = learn_from_execution_results(ExecutionResults, State),
    
    % Update state
    State#agency_state{
        environmental_model = NewEnvironmentalModel,
        learning_history = [LearningResults | State#agency_state.learning_history]
    }.

schedule_autonomous_cycle() ->
    % Schedule next autonomous cycle
    Interval = 10000, % 10 seconds
    erlang:send_after(Interval, self(), autonomous_cycle).

schedule_reflection_cycle() ->
    % Schedule next reflection cycle
    Interval = 60000, % 1 minute
    erlang:send_after(Interval, self(), reflection_cycle).

schedule_exploration_cycle() ->
    % Schedule next exploration cycle
    Interval = 30000, % 30 seconds
    erlang:send_after(Interval, self(), exploration_cycle).

%%====================================================================
%% Internal functions - Utility and Helper Functions
%%====================================================================

generate_agent_id() ->
    iolist_to_binary(io_lib:format("autonomous_agent_~p", [erlang:system_time(microsecond)])).

% Placeholder implementations for complex functions
get_system_processes() -> [].
get_memory_usage() -> #{total => 0, used => 0, free => 0}.
get_cpu_usage() -> 0.0.
get_active_connections() -> [].
get_recent_system_events() -> [].
analyze_directory_structure() -> #{}.
detect_file_changes() -> [].
categorize_file_types() -> #{}.
analyze_access_patterns() -> #{}.
get_network_connections() -> [].
analyze_network_traffic() -> #{}.
discover_network_services() -> [].
assess_connectivity() -> connected.
analyze_code_modules() -> [].
map_dependencies() -> #{}.
discover_api_endpoints() -> [].
analyze_data_structures() -> #{}.

extract_entities_from_perceptions(_Perceptions) -> #{}.
infer_relationships_from_perceptions(_Perceptions) -> #{}.
detect_temporal_patterns(_Perceptions, _Model) -> [].
identify_exploration_frontiers(_Perceptions, _Model) -> [].

detect_system_anomalies(_Model) -> [].
identify_knowledge_gaps(_Model) -> [].
calculate_exploration_priority(_Frontier) -> medium_priority.
calculate_learning_priority(_Gap) -> medium_priority.
compare_goal_priority(_Goal1, _Goal2) -> true.
create_goal_hierarchy(Goals) -> Goals.
select_active_pursuits(_Goals, _AutonomyLevel) -> #{}.

analyze_experience(_Experience, _State) -> #{}.
analyze_cognitive_processes(_Experience, _State) -> #{}.
extract_lessons_learned(_ExperienceAnalysis, _CognitiveAnalysis) -> [].
generate_self_insights(_Experience, _State) -> #{}.
generate_environmental_insights(_Experience, _State) -> #{}.
perform_meta_learning_analysis(_Experience, _State) -> #{}.
calculate_reflection_depth(_Experience, _State) -> 0.5.

analyze_current_cognitive_state(_CognitiveState) -> #{}.
analyze_goal_structure(_Goals) -> #{}.
analyze_behavioral_patterns(_Patterns) -> #{}.
analyze_knowledge_state(_KnowledgeGraph) -> #{}.
construct_self_model(_CognitiveAnalysis, _GoalAnalysis, _BehavioralAnalysis) -> #{}.

evaluate_current_goals(_Goals, _EnvironmentalModel) -> #{}.
make_autonomous_decisions(_GoalEvaluation, _State) -> [].
execute_autonomous_decisions(_Decisions, _State) -> #{}.
learn_from_execution_results(_ExecutionResults, _State) -> #{}.

save_agent_state(_State) -> ok.
adjust_autonomous_processes(_Level) -> ok.
summarize_cognitive_state(_CognitiveState) -> #{}.
get_knowledge_graph_size(_KnowledgeGraph) -> 0.
update_cognitive_state_from_perception(_Perceptions, CognitiveState) -> CognitiveState.
plan_exploration(_Strategy, _State) -> #{}.
execute_exploration_plan(_Plan, _State) -> #{}.
update_knowledge_graph_from_exploration(_Results, _KnowledgeGraph) -> ok.
integrate_exploration_results(_Results, EnvironmentalModel) -> EnvironmentalModel.
update_cognitive_state_from_reflection(_Insights, CognitiveState) -> CognitiveState.
update_behavioral_patterns(_Insights, Patterns) -> Patterns.
analyze_feedback_and_adapt(_Feedback, _Context, _State) -> #{}.
apply_behavioral_adaptations(_Adaptations, Patterns) -> Patterns.
construct_mental_model(_Observations, _State) -> #{}.
integrate_mental_model(_MentalModel, EnvironmentalModel) -> EnvironmentalModel.
update_knowledge_graph_from_mental_model(_MentalModel, _KnowledgeGraph) -> ok.
perform_causal_reasoning(_Event1, _Event2, _State) -> #{}.
update_causal_relationships(_Analysis, _EnvironmentalModel) -> #{}.
generate_creative_hypotheses(_Domain, _State) -> [].
evaluate_hypotheses(Hypotheses, _State) -> Hypotheses.
execute_hypothesis_test(_Hypothesis, _Strategy, _State) -> #{}.
update_beliefs_from_test(_TestResults, _State) -> #{}.
perform_bayesian_update(_Evidence, _Confidence, _State) -> #{}.
update_knowledge_graph_beliefs(_Beliefs, _KnowledgeGraph) -> ok.
update_meta_cognitive_awareness(_Results, _CognitiveState) -> #{}.
calculate_consciousness_level(_Results) -> 0.5.
evaluate_current_cognitive_state(_CognitiveState) -> #{}.
plan_cognitive_approach(_Objective, _State) -> #{}.
explore_domain_systematically(_Domain, _State) -> #{}.
integrate_domain_knowledge(_Results, _State) -> #{}.
discover_patterns_in_data(_Data, _State) -> [].
add_patterns_to_knowledge_graph(_Patterns, _KnowledgeGraph) -> ok.
form_concept_abstractions(_Examples, _State) -> [].
add_abstractions_to_knowledge_graph(_Abstractions, _KnowledgeGraph) -> ok.
generate_curiosity_goals(_Stimulus, _State) -> [].
execute_reflection_cycle(State) -> State.
execute_exploration_cycle(State) -> State.
generate_optimization_goals(_CognitiveState) -> [].
generate_creative_goals(_Context, _EnvironmentalModel) -> [].
generate_meta_goals(_CognitiveState) -> [].