-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) -> [].