-module(active_exploration_engine). -behaviour(gen_server). %% Active Exploration and Discovery Engine %% Sophisticated exploration system that enables autonomous agents to: %% - Actively explore their environment with purpose and curiosity %% - Discover new patterns, opportunities, and knowledge %% - Optimize exploration strategies based on discovery outcomes %% - Balance exploration vs exploitation trade-offs %% - Form and test hypotheses about the environment %% - Generate and pursue curiosity-driven investigations %% - Coordinate exploration with other cognitive processes -export([start_link/1, % Core exploration functions initiate_exploration/3, execute_exploration_strategy/3, evaluate_exploration_outcome/3, adaptive_exploration/2, curiosity_driven_exploration/2, hypothesis_driven_exploration/3, % Discovery and investigation investigate_anomaly/3, explore_knowledge_gap/2, discover_environmental_patterns/2, test_environmental_hypothesis/3, investigate_causal_relationships/3, % Exploration strategy management select_exploration_strategy/2, optimize_exploration_parameters/2, balance_exploration_exploitation/2, coordinate_exploration_activities/2, % Novelty and surprise processing detect_environmental_novelty/2, process_surprising_observations/3, generate_exploration_hypotheses/2, validate_exploration_discoveries/3, % Multi-modal exploration explore_spatial_environment/2, explore_temporal_patterns/2, explore_conceptual_space/2, explore_social_environment/2, % Active learning and experimentation design_exploration_experiments/3, conduct_exploration_experiments/2, analyze_experimental_results/3, iterative_hypothesis_refinement/3, % Exploration coordination coordinate_with_goal_formation/2, coordinate_with_learning/2, coordinate_with_reasoning/2, integrate_exploration_insights/2]). -export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]). %% Exploration and discovery data structures -record(exploration_strategy, { strategy_id, % Unique strategy identifier strategy_type, % Type of exploration strategy strategy_name, % Human-readable name strategy_parameters = #{}, % Parameters for the strategy exploration_scope, % Scope of exploration (spatial, temporal, conceptual) exploration_depth = 3, % Depth of exploration (1-10) exploration_breadth = 5, % Breadth of exploration (1-10) resource_requirements = #{}, % Resources needed for this strategy expected_discovery_types = [], % Types of discoveries expected success_criteria = [], % Criteria for successful exploration risk_assessment = #{}, % Risk analysis for the strategy effectiveness_history = [], % History of strategy effectiveness adaptation_rules = [], % Rules for adapting the strategy coordination_requirements = [] % Requirements for coordinating with other processes }). -record(exploration_target, { target_id, % Unique target identifier target_type, % Type of exploration target target_description, % Description of what to explore target_location, % Location (spatial, conceptual, etc.) novelty_score = 0.5, % How novel this target is (0-1) importance_score = 0.5, % How important exploring this is (0-1) accessibility_score = 0.5, % How accessible the target is (0-1) exploration_difficulty = 0.5, % Difficulty of exploring (0-1) resource_requirements = #{}, % Resources needed to explore previous_exploration_attempts = [], % Previous attempts to explore this expected_insights = [], % Expected insights from exploration related_targets = [], % Other targets related to this one discovery_potential = 0.5 % Potential for making discoveries (0-1) }). -record(exploration_discovery, { discovery_id, % Unique discovery identifier discovery_type, % Type of discovery made discovery_content, % Content of the discovery discovery_context, % Context in which discovery was made novelty_level = 0.5, % How novel the discovery is (0-1) significance_level = 0.5, % How significant the discovery is (0-1) confidence_level = 0.5, % Confidence in the discovery (0-1) verification_status = unverified, % Verification status supporting_evidence = [], % Evidence supporting the discovery contradicting_evidence = [], % Evidence contradicting the discovery implications = [], % Implications of the discovery follow_up_explorations = [], % Suggested follow-up explorations integration_requirements = [], % Requirements for integrating discovery discovery_timestamp, % When discovery was made discovery_location % Where discovery was made }). -record(curiosity_state, { current_curiosity_level = 0.5, % Current level of curiosity (0-1) curiosity_triggers = [], % Current triggers of curiosity areas_of_interest = [], % Current areas of high interest boredom_indicators = [], % Indicators of boredom surprise_history = [], % Recent surprising observations novelty_seeking_tendency = 0.5, % Tendency to seek novelty (0-1) exploration_motivation = 0.5, % Current motivation to explore (0-1) attention_focus = undefined, % Current focus of exploratory attention curiosity_satisfaction_level = 0.5 % How satisfied curiosity currently is (0-1) }). -record(exploration_state, { agent_id, % Associated agent active_explorations = #{}, % Currently active explorations exploration_strategies = [], % Available exploration strategies exploration_targets = #{}, % Current exploration targets exploration_discoveries = [], % Recent discoveries made curiosity_state = #curiosity_state{}, % Current curiosity state exploration_history = [], % History of exploration activities hypothesis_tracking = #{}, % Tracking of exploration hypotheses surprise_accumulator = [], % Accumulated surprising observations novelty_detector = #{}, % State of novelty detection exploration_performance = #{}, % Performance metrics for exploration coordination_state = #{}, % Coordination with other cognitive processes learning_from_exploration = #{}, % Learning accumulated from exploration exploration_preferences = #{} % Learned preferences about exploration }). %%==================================================================== %% API functions %%==================================================================== start_link(Config) -> AgentId = maps:get(agent_id, Config, generate_exploration_id()), io:format("[EXPLORATION] Starting active exploration engine for agent ~p~n", [AgentId]), gen_server:start_link(?MODULE, [AgentId, Config], []). %% Core exploration functions initiate_exploration(ExplorationPid, ExplorationTarget, Context) -> gen_server:call(ExplorationPid, {initiate_exploration, ExplorationTarget, Context}). execute_exploration_strategy(ExplorationPid, Strategy, Target) -> gen_server:call(ExplorationPid, {execute_exploration_strategy, Strategy, Target}). evaluate_exploration_outcome(ExplorationPid, ExplorationId, Outcome) -> gen_server:call(ExplorationPid, {evaluate_exploration_outcome, ExplorationId, Outcome}). adaptive_exploration(ExplorationPid, AdaptationContext) -> gen_server:call(ExplorationPid, {adaptive_exploration, AdaptationContext}). curiosity_driven_exploration(ExplorationPid, CuriosityTrigger) -> gen_server:call(ExplorationPid, {curiosity_driven_exploration, CuriosityTrigger}). hypothesis_driven_exploration(ExplorationPid, Hypothesis, TestingStrategy) -> gen_server:call(ExplorationPid, {hypothesis_driven_exploration, Hypothesis, TestingStrategy}). %% Discovery and investigation investigate_anomaly(ExplorationPid, Anomaly, InvestigationScope) -> gen_server:call(ExplorationPid, {investigate_anomaly, Anomaly, InvestigationScope}). explore_knowledge_gap(ExplorationPid, KnowledgeGap) -> gen_server:call(ExplorationPid, {explore_knowledge_gap, KnowledgeGap}). discover_environmental_patterns(ExplorationPid, SearchCriteria) -> gen_server:call(ExplorationPid, {discover_environmental_patterns, SearchCriteria}). test_environmental_hypothesis(ExplorationPid, Hypothesis, TestDesign) -> gen_server:call(ExplorationPid, {test_environmental_hypothesis, Hypothesis, TestDesign}). investigate_causal_relationships(ExplorationPid, CausalHypothesis, InvestigationMethod) -> gen_server:call(ExplorationPid, {investigate_causal_relationships, CausalHypothesis, InvestigationMethod}). %% Exploration strategy management select_exploration_strategy(ExplorationPid, SelectionCriteria) -> gen_server:call(ExplorationPid, {select_exploration_strategy, SelectionCriteria}). optimize_exploration_parameters(ExplorationPid, OptimizationObjective) -> gen_server:call(ExplorationPid, {optimize_exploration_parameters, OptimizationObjective}). balance_exploration_exploitation(ExplorationPid, BalancingContext) -> gen_server:call(ExplorationPid, {balance_exploration_exploitation, BalancingContext}). coordinate_exploration_activities(ExplorationPid, CoordinationRequest) -> gen_server:call(ExplorationPid, {coordinate_exploration_activities, CoordinationRequest}). %% Novelty and surprise processing detect_environmental_novelty(ExplorationPid, EnvironmentalInput) -> gen_server:call(ExplorationPid, {detect_environmental_novelty, EnvironmentalInput}). process_surprising_observations(ExplorationPid, Observation, ExpectedOutcome) -> gen_server:cast(ExplorationPid, {process_surprising_observations, Observation, ExpectedOutcome}). generate_exploration_hypotheses(ExplorationPid, ObservationContext) -> gen_server:call(ExplorationPid, {generate_exploration_hypotheses, ObservationContext}). validate_exploration_discoveries(ExplorationPid, Discovery, ValidationCriteria) -> gen_server:call(ExplorationPid, {validate_exploration_discoveries, Discovery, ValidationCriteria}). %% Multi-modal exploration explore_spatial_environment(ExplorationPid, SpatialContext) -> gen_server:call(ExplorationPid, {explore_spatial_environment, SpatialContext}). explore_temporal_patterns(ExplorationPid, TemporalContext) -> gen_server:call(ExplorationPid, {explore_temporal_patterns, TemporalContext}). explore_conceptual_space(ExplorationPid, ConceptualContext) -> gen_server:call(ExplorationPid, {explore_conceptual_space, ConceptualContext}). explore_social_environment(ExplorationPid, SocialContext) -> gen_server:call(ExplorationPid, {explore_social_environment, SocialContext}). %% Active learning and experimentation design_exploration_experiments(ExplorationPid, ExperimentObjective, Constraints) -> gen_server:call(ExplorationPid, {design_exploration_experiments, ExperimentObjective, Constraints}). conduct_exploration_experiments(ExplorationPid, ExperimentDesign) -> gen_server:call(ExplorationPid, {conduct_exploration_experiments, ExperimentDesign}). analyze_experimental_results(ExplorationPid, ExperimentId, Results) -> gen_server:call(ExplorationPid, {analyze_experimental_results, ExperimentId, Results}). iterative_hypothesis_refinement(ExplorationPid, HypothesisId, RefinementData) -> gen_server:call(ExplorationPid, {iterative_hypothesis_refinement, HypothesisId, RefinementData}). %% Exploration coordination coordinate_with_goal_formation(ExplorationPid, GoalFormationRequest) -> gen_server:call(ExplorationPid, {coordinate_with_goal_formation, GoalFormationRequest}). coordinate_with_learning(ExplorationPid, LearningRequest) -> gen_server:call(ExplorationPid, {coordinate_with_learning, LearningRequest}). coordinate_with_reasoning(ExplorationPid, ReasoningRequest) -> gen_server:call(ExplorationPid, {coordinate_with_reasoning, ReasoningRequest}). integrate_exploration_insights(ExplorationPid, InsightIntegrationRequest) -> gen_server:call(ExplorationPid, {integrate_exploration_insights, InsightIntegrationRequest}). %%==================================================================== %% gen_server callbacks %%==================================================================== init([AgentId, Config]) -> process_flag(trap_exit, true), io:format("[EXPLORATION] Initializing active exploration engine for agent ~p~n", [AgentId]), % Initialize exploration strategies ExplorationStrategies = initialize_exploration_strategies(Config), % Initialize curiosity state CuriosityState = initialize_curiosity_state(Config), % Initialize novelty detection NoveltyDetector = initialize_novelty_detector(Config), State = #exploration_state{ agent_id = AgentId, exploration_strategies = ExplorationStrategies, curiosity_state = CuriosityState, novelty_detector = NoveltyDetector }, % Start exploration cycles schedule_exploration_cycle(), schedule_curiosity_cycle(), schedule_discovery_analysis_cycle(), {ok, State}. handle_call({initiate_exploration, ExplorationTarget, Context}, _From, State) -> io:format("[EXPLORATION] Initiating exploration of target: ~p~n", [ExplorationTarget]), % Analyze exploration target TargetAnalysis = analyze_exploration_target(ExplorationTarget, Context, State), % Select appropriate exploration strategy ExplorationStrategy = select_strategy_for_target(ExplorationTarget, TargetAnalysis, State), % Create exploration plan ExplorationPlan = create_exploration_plan(ExplorationTarget, ExplorationStrategy, Context, State), % Begin exploration execution ExplorationId = begin_exploration_execution(ExplorationPlan, State), % Update active explorations NewActiveExplorations = maps:put(ExplorationId, ExplorationPlan, State#exploration_state.active_explorations), NewState = State#exploration_state{active_explorations = NewActiveExplorations}, {reply, {ok, ExplorationId, ExplorationPlan}, NewState}; handle_call({execute_exploration_strategy, Strategy, Target}, _From, State) -> io:format("[EXPLORATION] Executing exploration strategy: ~p for target: ~p~n", [Strategy, Target]), % Execute the specified strategy ExecutionResult = execute_strategy_implementation(Strategy, Target, State), % Process execution results ProcessedResults = process_exploration_results(ExecutionResult, Strategy, Target, State), % Update exploration performance metrics UpdatedPerformance = update_exploration_performance(Strategy, ProcessedResults, State#exploration_state.exploration_performance), NewState = State#exploration_state{exploration_performance = UpdatedPerformance}, {reply, {ok, ProcessedResults}, NewState}; handle_call({curiosity_driven_exploration, CuriosityTrigger}, _From, State) -> io:format("[EXPLORATION] Curiosity-driven exploration triggered by: ~p~n", [CuriosityTrigger]), % Analyze curiosity trigger CuriosityAnalysis = analyze_curiosity_trigger(CuriosityTrigger, State), % Generate curiosity-driven exploration targets CuriosityTargets = generate_curiosity_targets(CuriosityAnalysis, State), % Prioritize curiosity targets PrioritizedTargets = prioritize_curiosity_targets(CuriosityTargets, State), % Initiate exploration of highest priority target SelectedTarget = select_highest_priority_target(PrioritizedTargets), ExplorationResult = initiate_curiosity_exploration(SelectedTarget, CuriosityAnalysis, State), % Update curiosity state NewCuriosityState = update_curiosity_state_from_exploration(CuriosityTrigger, ExplorationResult, State#exploration_state.curiosity_state), NewState = State#exploration_state{curiosity_state = NewCuriosityState}, {reply, {ok, ExplorationResult}, NewState}; handle_call({investigate_anomaly, Anomaly, InvestigationScope}, _From, State) -> io:format("[EXPLORATION] Investigating anomaly: ~p~n", [Anomaly]), % Analyze the anomaly AnomalyAnalysis = analyze_anomaly_characteristics(Anomaly, InvestigationScope, State), % Design investigation strategy InvestigationStrategy = design_anomaly_investigation(AnomalyAnalysis, State), % Execute investigation InvestigationResults = execute_anomaly_investigation(InvestigationStrategy, Anomaly, State), % Process investigation findings ProcessedFindings = process_investigation_findings(InvestigationResults, State), % Generate follow-up investigations if needed FollowUpInvestigations = generate_followup_investigations(ProcessedFindings, State), Result = #{ anomaly_analysis => AnomalyAnalysis, investigation_results => InvestigationResults, processed_findings => ProcessedFindings, follow_up_investigations => FollowUpInvestigations }, {reply, {ok, Result}, State}; handle_call({discover_environmental_patterns, SearchCriteria}, _From, State) -> io:format("[EXPLORATION] Discovering environmental patterns with criteria: ~p~n", [SearchCriteria]), % Collect relevant exploration data ExplorationData = collect_exploration_data_for_pattern_discovery(SearchCriteria, State), % Apply pattern discovery algorithms DiscoveredPatterns = apply_pattern_discovery_algorithms(ExplorationData, SearchCriteria, State), % Validate discovered patterns ValidatedPatterns = validate_discovered_patterns(DiscoveredPatterns, State), % Assess pattern significance PatternSignificance = assess_pattern_significance(ValidatedPatterns, State), % Generate pattern-based hypotheses PatternHypotheses = generate_pattern_hypotheses_from_patterns(ValidatedPatterns, State), Result = #{ discovered_patterns => ValidatedPatterns, pattern_significance => PatternSignificance, generated_hypotheses => PatternHypotheses }, {reply, {ok, Result}, State}; handle_call({hypothesis_driven_exploration, Hypothesis, TestingStrategy}, _From, State) -> io:format("[EXPLORATION] Hypothesis-driven exploration: ~p~n", [Hypothesis]), % Analyze hypothesis for exploration requirements HypothesisAnalysis = analyze_hypothesis_for_exploration(Hypothesis, TestingStrategy, State), % Design hypothesis testing exploration TestingExploration = design_hypothesis_testing_exploration(HypothesisAnalysis, State), % Execute hypothesis testing TestingResults = execute_hypothesis_testing(TestingExploration, Hypothesis, State), % Analyze testing results ResultAnalysis = analyze_hypothesis_testing_results(TestingResults, Hypothesis, State), % Update hypothesis tracking HypothesisId = maps:get(hypothesis_id, Hypothesis, generate_hypothesis_id()), UpdatedHypothesisTracking = update_hypothesis_tracking(HypothesisId, ResultAnalysis, State#exploration_state.hypothesis_tracking), NewState = State#exploration_state{hypothesis_tracking = UpdatedHypothesisTracking}, {reply, {ok, ResultAnalysis}, NewState}; handle_call({detect_environmental_novelty, EnvironmentalInput}, _From, State) -> io:format("[EXPLORATION] Detecting environmental novelty~n"), % Apply novelty detection algorithms NoveltyDetectionResult = apply_novelty_detection(EnvironmentalInput, State#exploration_state.novelty_detector), % Update novelty detector state UpdatedNoveltyDetector = update_novelty_detector(EnvironmentalInput, NoveltyDetectionResult, State#exploration_state.novelty_detector), % Process detected novelty ProcessedNovelty = process_detected_novelty(NoveltyDetectionResult, EnvironmentalInput, State), % Generate exploration targets from novelty NoveltyTargets = generate_targets_from_novelty(ProcessedNovelty, State), NewState = State#exploration_state{novelty_detector = UpdatedNoveltyDetector}, Result = #{ novelty_detected => NoveltyDetectionResult, processed_novelty => ProcessedNovelty, exploration_targets => NoveltyTargets }, {reply, {ok, Result}, NewState}; handle_call({explore_conceptual_space, ConceptualContext}, _From, State) -> io:format("[EXPLORATION] Exploring conceptual space: ~p~n", [ConceptualContext]), % Map conceptual space for exploration ConceptualMap = map_conceptual_space(ConceptualContext, State), % Identify interesting regions in conceptual space InterestingRegions = identify_interesting_conceptual_regions(ConceptualMap, State), % Select regions for exploration SelectedRegions = select_conceptual_regions_for_exploration(InterestingRegions, State), % Explore selected conceptual regions ExplorationResults = explore_conceptual_regions(SelectedRegions, ConceptualContext, State), % Process conceptual discoveries ConceptualDiscoveries = process_conceptual_exploration_results(ExplorationResults, State), Result = #{ conceptual_map => ConceptualMap, explored_regions => SelectedRegions, exploration_results => ExplorationResults, conceptual_discoveries => ConceptualDiscoveries }, {reply, {ok, Result}, State}; handle_call({design_exploration_experiments, ExperimentObjective, Constraints}, _From, State) -> io:format("[EXPLORATION] Designing exploration experiments for objective: ~p~n", [ExperimentObjective]), % Analyze experiment objective ObjectiveAnalysis = analyze_experiment_objective(ExperimentObjective, Constraints, State), % Generate experiment design alternatives ExperimentDesigns = generate_experiment_designs(ObjectiveAnalysis, State), % Evaluate experiment designs EvaluatedDesigns = evaluate_experiment_designs(ExperimentDesigns, Constraints, State), % Select optimal experiment design OptimalDesign = select_optimal_experiment_design(EvaluatedDesigns, State), % Refine experiment design RefinedDesign = refine_experiment_design(OptimalDesign, State), {reply, {ok, RefinedDesign}, State}; handle_call({balance_exploration_exploitation, BalancingContext}, _From, State) -> io:format("[EXPLORATION] Balancing exploration vs exploitation~n"), % Analyze current exploration-exploitation balance CurrentBalance = analyze_current_exploration_exploitation_balance(State), % Determine optimal balance for context OptimalBalance = determine_optimal_balance(BalancingContext, State), % Calculate balance adjustment BalanceAdjustment = calculate_balance_adjustment(CurrentBalance, OptimalBalance), % Apply balance adjustment AdjustedState = apply_exploration_exploitation_adjustment(BalanceAdjustment, State), Result = #{ current_balance => CurrentBalance, optimal_balance => OptimalBalance, adjustment => BalanceAdjustment }, {reply, {ok, Result}, AdjustedState}; handle_call(_Request, _From, State) -> {reply, {error, unknown_request}, State}. handle_cast({process_surprising_observations, Observation, ExpectedOutcome}, State) -> io:format("[EXPLORATION] Processing surprising observation~n"), % Calculate surprise magnitude SurpriseMagnitude = calculate_surprise_magnitude(Observation, ExpectedOutcome), % Create surprise record SurpriseRecord = create_surprise_record(Observation, ExpectedOutcome, SurpriseMagnitude), % Add to surprise accumulator UpdatedSurpriseAccumulator = [SurpriseRecord | State#exploration_state.surprise_accumulator], % Update curiosity state based on surprise UpdatedCuriosityState = update_curiosity_from_surprise(SurpriseRecord, State#exploration_state.curiosity_state), % Generate surprise-driven exploration targets _SurpriseTargets = generate_surprise_driven_targets(SurpriseRecord, State), NewState = State#exploration_state{ surprise_accumulator = UpdatedSurpriseAccumulator, curiosity_state = UpdatedCuriosityState }, {noreply, NewState}; handle_cast(_Msg, State) -> {noreply, State}. handle_info(exploration_cycle, State) -> % Periodic exploration cycle NewState = perform_exploration_cycle(State), schedule_exploration_cycle(), {noreply, NewState}; handle_info(curiosity_cycle, State) -> % Periodic curiosity processing cycle NewState = perform_curiosity_cycle(State), schedule_curiosity_cycle(), {noreply, NewState}; handle_info(discovery_analysis_cycle, State) -> % Periodic discovery analysis cycle NewState = perform_discovery_analysis_cycle(State), schedule_discovery_analysis_cycle(), {noreply, NewState}; handle_info(_Info, State) -> {noreply, State}. terminate(_Reason, State) -> io:format("[EXPLORATION] Active exploration engine for agent ~p terminating~n", [State#exploration_state.agent_id]), save_exploration_state(State), ok. code_change(_OldVsn, State, _Extra) -> {ok, State}. %%==================================================================== %% Internal functions - Exploration Strategy Implementation %%==================================================================== analyze_exploration_target(Target, Context, State) -> % Analyze exploration target to determine exploration approach % Assess target novelty NoveltyScore = assess_target_novelty(Target, State), % Assess target accessibility AccessibilityScore = assess_target_accessibility(Target, Context, State), % Assess potential insights InsightPotential = assess_insight_potential(Target, State), % Assess resource requirements ResourceRequirements = assess_target_resource_requirements(Target, State), % Assess risks RiskAssessment = assess_exploration_risks(Target, Context, State), #{ novelty_score => NoveltyScore, accessibility_score => AccessibilityScore, insight_potential => InsightPotential, resource_requirements => ResourceRequirements, risk_assessment => RiskAssessment, overall_attractiveness => calculate_target_attractiveness(NoveltyScore, AccessibilityScore, InsightPotential) }. select_strategy_for_target(Target, TargetAnalysis, State) -> % Select the most appropriate exploration strategy for the target AvailableStrategies = State#exploration_state.exploration_strategies, % Score strategies for this target StrategyScores = score_strategies_for_target(Target, TargetAnalysis, AvailableStrategies), % Select best strategy BestStrategy = select_best_exploration_strategy(StrategyScores), BestStrategy. create_exploration_plan(Target, Strategy, Context, State) -> % Create detailed exploration plan % Generate exploration steps ExplorationSteps = generate_exploration_steps(Target, Strategy, Context, State), % Estimate resource requirements ResourceEstimate = estimate_exploration_resources(ExplorationSteps, State), % Estimate time requirements TimeEstimate = estimate_exploration_time(ExplorationSteps, State), % Identify potential obstacles PotentialObstacles = identify_exploration_obstacles(ExplorationSteps, State), % Create contingency plans ContingencyPlans = create_exploration_contingencies(PotentialObstacles, State), #{ target => Target, strategy => Strategy, exploration_steps => ExplorationSteps, resource_estimate => ResourceEstimate, time_estimate => TimeEstimate, potential_obstacles => PotentialObstacles, contingency_plans => ContingencyPlans, plan_id => generate_exploration_plan_id() }. execute_strategy_implementation(Strategy, Target, State) -> % Execute the specific implementation of an exploration strategy case Strategy#exploration_strategy.strategy_type of systematic_exploration -> execute_systematic_exploration(Strategy, Target, State); random_exploration -> execute_random_exploration(Strategy, Target, State); hypothesis_driven -> execute_hypothesis_driven_exploration(Strategy, Target, State); curiosity_driven -> execute_curiosity_driven_exploration(Strategy, Target, State); adaptive_exploration -> execute_adaptive_exploration(Strategy, Target, State); multi_modal_exploration -> execute_multi_modal_exploration(Strategy, Target, State); _ -> execute_general_exploration(Strategy, Target, State) end. %%==================================================================== %% Internal functions - Novelty Detection and Curiosity %%==================================================================== apply_novelty_detection(Input, NoveltyDetector) -> % Apply novelty detection algorithms to input % Statistical novelty detection StatisticalNovelty = detect_statistical_novelty(Input, NoveltyDetector), % Semantic novelty detection SemanticNovelty = detect_semantic_novelty(Input, NoveltyDetector), % Structural novelty detection StructuralNovelty = detect_structural_novelty(Input, NoveltyDetector), % Contextual novelty detection ContextualNovelty = detect_contextual_novelty(Input, NoveltyDetector), % Combine novelty scores OverallNovelty = combine_novelty_scores(StatisticalNovelty, SemanticNovelty, StructuralNovelty, ContextualNovelty), #{ statistical_novelty => StatisticalNovelty, semantic_novelty => SemanticNovelty, structural_novelty => StructuralNovelty, contextual_novelty => ContextualNovelty, overall_novelty => OverallNovelty, novelty_threshold_exceeded => OverallNovelty > 0.7 }. analyze_curiosity_trigger(Trigger, State) -> % Analyze what triggered curiosity and why % Identify trigger type TriggerType = classify_curiosity_trigger(Trigger), % Assess trigger intensity TriggerIntensity = assess_trigger_intensity(Trigger, State), % Identify related areas of interest RelatedInterests = identify_related_interests(Trigger, State#exploration_state.curiosity_state), % Assess exploration potential ExplorationPotential = assess_curiosity_exploration_potential(Trigger, State), #{ trigger_type => TriggerType, trigger_intensity => TriggerIntensity, related_interests => RelatedInterests, exploration_potential => ExplorationPotential, analysis_timestamp => erlang:system_time(second) }. generate_curiosity_targets(CuriosityAnalysis, State) -> % Generate specific targets for curiosity-driven exploration TriggerType = maps:get(trigger_type, CuriosityAnalysis), % Generate targets based on trigger type Targets = case TriggerType of novelty_trigger -> generate_novelty_exploration_targets(CuriosityAnalysis, State); surprise_trigger -> generate_surprise_exploration_targets(CuriosityAnalysis, State); gap_trigger -> generate_gap_exploration_targets(CuriosityAnalysis, State); inconsistency_trigger -> generate_inconsistency_exploration_targets(CuriosityAnalysis, State); _ -> generate_general_curiosity_targets(CuriosityAnalysis, State) end, Targets. %%==================================================================== %% Internal functions - Pattern Discovery and Analysis %%==================================================================== apply_pattern_discovery_algorithms(ExplorationData, SearchCriteria, State) -> % Apply various pattern discovery algorithms to exploration data % Temporal pattern discovery TemporalPatterns = discover_temporal_exploration_patterns(ExplorationData, State), % Spatial pattern discovery SpatialPatterns = discover_spatial_exploration_patterns(ExplorationData, State), % Causal pattern discovery CausalPatterns = discover_causal_exploration_patterns(ExplorationData, State), % Association pattern discovery AssociationPatterns = discover_association_patterns(ExplorationData, State), % Anomaly pattern discovery AnomalyPatterns = discover_anomaly_patterns(ExplorationData, State), AllPatterns = #{ temporal => TemporalPatterns, spatial => SpatialPatterns, causal => CausalPatterns, association => AssociationPatterns, anomaly => AnomalyPatterns }, % Filter patterns based on search criteria FilteredPatterns = filter_patterns_by_criteria(AllPatterns, SearchCriteria), FilteredPatterns. validate_discovered_patterns(Patterns, State) -> % Validate discovered patterns using various validation methods % Statistical validation StatisticalValidation = validate_patterns_statistically(Patterns, State), % Cross-validation CrossValidation = cross_validate_patterns(Patterns, State), % Consistency validation ConsistencyValidation = validate_pattern_consistency(Patterns, State), % Combine validation results ValidatedPatterns = combine_pattern_validations(Patterns, StatisticalValidation, CrossValidation, ConsistencyValidation), ValidatedPatterns. assess_pattern_significance(Patterns, State) -> % Assess the significance of discovered patterns lists:map(fun(Pattern) -> % Assess novelty significance NoveltySignificance = assess_pattern_novelty_significance(Pattern, State), % Assess practical significance PracticalSignificance = assess_pattern_practical_significance(Pattern, State), % Assess theoretical significance TheoreticalSignificance = assess_pattern_theoretical_significance(Pattern, State), % Combine significance scores OverallSignificance = combine_significance_scores(NoveltySignificance, PracticalSignificance, TheoreticalSignificance), maps:put(significance_score, OverallSignificance, Pattern) end, Patterns). %%==================================================================== %% Internal functions - Hypothesis Testing and Experimentation %%==================================================================== design_hypothesis_testing_exploration(HypothesisAnalysis, State) -> % Design exploration specifically to test a hypothesis % Identify testable predictions TestablePredictions = identify_testable_predictions(HypothesisAnalysis), % Design tests for each prediction TestDesigns = design_prediction_tests(TestablePredictions, State), % Optimize test design OptimizedTestDesign = optimize_hypothesis_test_design(TestDesigns, State), % Create execution plan ExecutionPlan = create_hypothesis_test_execution_plan(OptimizedTestDesign, State), #{ hypothesis_analysis => HypothesisAnalysis, testable_predictions => TestablePredictions, test_designs => TestDesigns, optimized_design => OptimizedTestDesign, execution_plan => ExecutionPlan }. execute_hypothesis_testing(TestingExploration, Hypothesis, State) -> % Execute hypothesis testing exploration ExecutionPlan = maps:get(execution_plan, TestingExploration), % Execute each test in the plan TestResults = execute_hypothesis_tests(ExecutionPlan, State), % Collect additional observational data ObservationalData = collect_hypothesis_observational_data(Hypothesis, State), % Combine test results and observational data CombinedResults = combine_hypothesis_test_data(TestResults, ObservationalData), CombinedResults. analyze_hypothesis_testing_results(TestingResults, Hypothesis, State) -> % Analyze results of hypothesis testing % Statistical analysis of results StatisticalAnalysis = perform_statistical_analysis_of_results(TestingResults), % Assess hypothesis support HypothesisSupport = assess_hypothesis_support(TestingResults, Hypothesis, StatisticalAnalysis), % Identify alternative explanations AlternativeExplanations = identify_alternative_explanations(TestingResults, State), % Generate follow-up hypotheses FollowUpHypotheses = generate_followup_hypotheses(TestingResults, Hypothesis, State), % Calculate confidence in conclusions ConclusionConfidence = calculate_hypothesis_conclusion_confidence(HypothesisSupport, AlternativeExplanations), #{ statistical_analysis => StatisticalAnalysis, hypothesis_support => HypothesisSupport, alternative_explanations => AlternativeExplanations, follow_up_hypotheses => FollowUpHypotheses, conclusion_confidence => ConclusionConfidence, overall_assessment => assess_overall_hypothesis_outcome(HypothesisSupport, ConclusionConfidence) }. %%==================================================================== %% Internal functions - Exploration Cycles and Coordination %%==================================================================== schedule_exploration_cycle() -> Interval = 45000, % 45 seconds erlang:send_after(Interval, self(), exploration_cycle). schedule_curiosity_cycle() -> Interval = 30000, % 30 seconds erlang:send_after(Interval, self(), curiosity_cycle). schedule_discovery_analysis_cycle() -> Interval = 120000, % 2 minutes erlang:send_after(Interval, self(), discovery_analysis_cycle). perform_exploration_cycle(State) -> % Perform periodic exploration cycle % Evaluate ongoing explorations EvaluatedExplorations = evaluate_ongoing_explorations(State), % Identify new exploration opportunities NewOpportunities = identify_new_exploration_opportunities(State), % Select new explorations to initiate SelectedExplorations = select_new_explorations(NewOpportunities, State), % Update exploration targets UpdatedTargets = update_exploration_targets(EvaluatedExplorations, SelectedExplorations, State#exploration_state.exploration_targets), % Update exploration history UpdatedHistory = update_exploration_history(EvaluatedExplorations, State#exploration_state.exploration_history), State#exploration_state{ exploration_targets = UpdatedTargets, exploration_history = UpdatedHistory }. perform_curiosity_cycle(State) -> % Perform periodic curiosity processing % Update curiosity level based on recent experiences UpdatedCuriosity = update_curiosity_level(State#exploration_state.curiosity_state, State), % Process accumulated surprises ProcessedSurprises = process_accumulated_surprises(State#exploration_state.surprise_accumulator), % Generate new areas of interest NewInterestAreas = generate_new_interest_areas(UpdatedCuriosity, ProcessedSurprises, State), % Update areas of interest UpdatedInterestAreas = update_areas_of_interest(NewInterestAreas, UpdatedCuriosity#curiosity_state.areas_of_interest), FinalCuriosityState = UpdatedCuriosity#curiosity_state{areas_of_interest = UpdatedInterestAreas}, State#exploration_state{curiosity_state = FinalCuriosityState}. perform_discovery_analysis_cycle(State) -> % Perform periodic analysis of discoveries RecentDiscoveries = get_recent_discoveries(State, 20), if length(RecentDiscoveries) > 0 -> % Analyze discovery patterns DiscoveryPatterns = analyze_discovery_patterns(RecentDiscoveries, State), % Identify discovery trends DiscoveryTrends = identify_discovery_trends(RecentDiscoveries, State), % Update exploration preferences based on discoveries UpdatedPreferences = update_exploration_preferences_from_discoveries(DiscoveryPatterns, DiscoveryTrends, State#exploration_state.exploration_preferences), State#exploration_state{exploration_preferences = UpdatedPreferences}; true -> State end. %%==================================================================== %% Internal functions - Utility and Helper Functions %%==================================================================== initialize_exploration_strategies(_Config) -> [ create_strategy(systematic_exploration, "Systematic exploration of environment"), create_strategy(random_exploration, "Random exploration with curiosity guidance"), create_strategy(hypothesis_driven, "Hypothesis-driven exploration"), create_strategy(curiosity_driven, "Curiosity-driven exploration"), create_strategy(adaptive_exploration, "Adaptive exploration based on outcomes"), create_strategy(multi_modal_exploration, "Multi-modal exploration across dimensions") ]. create_strategy(Type, Description) -> #exploration_strategy{ strategy_id = generate_strategy_id(), strategy_type = Type, strategy_name = Description, strategy_parameters = initialize_strategy_parameters(Type) }. initialize_strategy_parameters(Type) -> case Type of systematic_exploration -> #{thoroughness => 0.8, coverage => 0.9}; random_exploration -> #{randomness => 0.7, curiosity_bias => 0.6}; hypothesis_driven -> #{rigor => 0.9, prediction_focus => 0.8}; curiosity_driven -> #{novelty_seeking => 0.8, surprise_sensitivity => 0.7}; adaptive_exploration -> #{adaptation_rate => 0.6, learning_rate => 0.5}; multi_modal_exploration -> #{dimension_coverage => 0.7, integration_depth => 0.6}; _ -> #{general_effectiveness => 0.5} end. initialize_curiosity_state(_Config) -> #curiosity_state{ current_curiosity_level = 0.6, novelty_seeking_tendency = 0.7, exploration_motivation = 0.5, curiosity_satisfaction_level = 0.4 }. initialize_novelty_detector(_Config) -> #{ statistical_baseline => #{}, semantic_memory => [], structural_patterns => [], contextual_history => [], detection_threshold => 0.6, adaptation_rate => 0.1 }. generate_exploration_id() -> iolist_to_binary(io_lib:format("exploration_~p", [erlang:system_time(microsecond)])). generate_strategy_id() -> iolist_to_binary(io_lib:format("strategy_~p", [erlang:system_time(microsecond)])). generate_exploration_plan_id() -> iolist_to_binary(io_lib:format("plan_~p", [erlang:system_time(microsecond)])). generate_hypothesis_id() -> iolist_to_binary(io_lib:format("hypothesis_~p", [erlang:system_time(microsecond)])). save_exploration_state(_State) -> % Save exploration state to persistent storage ok. % Placeholder implementations for complex functions (would be fully implemented in production) assess_target_novelty(_Target, _State) -> 0.7. assess_target_accessibility(_Target, _Context, _State) -> 0.8. assess_insight_potential(_Target, _State) -> 0.6. assess_target_resource_requirements(_Target, _State) -> #{}. assess_exploration_risks(_Target, _Context, _State) -> #{}. calculate_target_attractiveness(Novelty, Accessibility, Insight) -> (Novelty + Accessibility + Insight) / 3. score_strategies_for_target(_Target, _Analysis, Strategies) -> [{S, 0.5} || S <- Strategies]. select_best_exploration_strategy(Scores) -> element(1, hd(Scores)). generate_exploration_steps(_Target, _Strategy, _Context, _State) -> []. estimate_exploration_resources(_Steps, _State) -> #{}. estimate_exploration_time(_Steps, _State) -> 60. identify_exploration_obstacles(_Steps, _State) -> []. create_exploration_contingencies(_Obstacles, _State) -> []. begin_exploration_execution(_Plan, _State) -> generate_exploration_id(). execute_systematic_exploration(_Strategy, _Target, _State) -> #{type => systematic}. execute_random_exploration(_Strategy, _Target, _State) -> #{type => random}. execute_hypothesis_driven_exploration(_Strategy, _Target, _State) -> #{type => hypothesis_driven}. execute_curiosity_driven_exploration(_Strategy, _Target, _State) -> #{type => curiosity_driven}. execute_adaptive_exploration(_Strategy, _Target, _State) -> #{type => adaptive}. execute_multi_modal_exploration(_Strategy, _Target, _State) -> #{type => multi_modal}. execute_general_exploration(_Strategy, _Target, _State) -> #{type => general}. process_exploration_results(_Result, _Strategy, _Target, _State) -> #{}. update_exploration_performance(_Strategy, _Results, Performance) -> Performance. detect_statistical_novelty(_Input, _Detector) -> 0.5. detect_semantic_novelty(_Input, _Detector) -> 0.4. detect_structural_novelty(_Input, _Detector) -> 0.6. detect_contextual_novelty(_Input, _Detector) -> 0.3. combine_novelty_scores(Stat, Sem, Struct, Cont) -> (Stat + Sem + Struct + Cont) / 4. update_novelty_detector(_Input, _Result, Detector) -> Detector. process_detected_novelty(_Result, _Input, _State) -> #{}. generate_targets_from_novelty(_Novelty, _State) -> []. classify_curiosity_trigger(_Trigger) -> novelty_trigger. assess_trigger_intensity(_Trigger, _State) -> 0.7. identify_related_interests(_Trigger, _CuriosityState) -> []. assess_curiosity_exploration_potential(_Trigger, _State) -> 0.6. generate_novelty_exploration_targets(_Analysis, _State) -> []. generate_surprise_exploration_targets(_Analysis, _State) -> []. generate_gap_exploration_targets(_Analysis, _State) -> []. generate_inconsistency_exploration_targets(_Analysis, _State) -> []. generate_general_curiosity_targets(_Analysis, _State) -> []. prioritize_curiosity_targets(Targets, _State) -> Targets. select_highest_priority_target(Targets) -> hd(Targets ++ [undefined]). initiate_curiosity_exploration(_Target, _Analysis, _State) -> #{}. update_curiosity_state_from_exploration(_Trigger, _Result, CuriosityState) -> CuriosityState. calculate_surprise_magnitude(_Observation, _Expected) -> 0.6. create_surprise_record(Observation, Expected, Magnitude) -> #{observation => Observation, expected => Expected, magnitude => Magnitude}. update_curiosity_from_surprise(_Record, CuriosityState) -> CuriosityState. generate_surprise_driven_targets(_Record, _State) -> []. analyze_anomaly_characteristics(_Anomaly, _Scope, _State) -> #{}. design_anomaly_investigation(_Analysis, _State) -> #{}. execute_anomaly_investigation(_Strategy, _Anomaly, _State) -> #{}. process_investigation_findings(_Results, _State) -> #{}. generate_followup_investigations(_Findings, _State) -> []. collect_exploration_data_for_pattern_discovery(_Criteria, _State) -> []. discover_temporal_exploration_patterns(_Data, _State) -> []. discover_spatial_exploration_patterns(_Data, _State) -> []. discover_causal_exploration_patterns(_Data, _State) -> []. discover_association_patterns(_Data, _State) -> []. discover_anomaly_patterns(_Data, _State) -> []. filter_patterns_by_criteria(Patterns, _Criteria) -> Patterns. validate_patterns_statistically(_Patterns, _State) -> #{}. cross_validate_patterns(_Patterns, _State) -> #{}. validate_pattern_consistency(_Patterns, _State) -> #{}. combine_pattern_validations(Patterns, _Stat, _Cross, _Consistency) -> Patterns. assess_pattern_novelty_significance(_Pattern, _State) -> 0.6. assess_pattern_practical_significance(_Pattern, _State) -> 0.5. assess_pattern_theoretical_significance(_Pattern, _State) -> 0.7. combine_significance_scores(Nov, Prac, Theo) -> (Nov + Prac + Theo) / 3. analyze_hypothesis_for_exploration(_Hypothesis, _Strategy, _State) -> #{}. identify_testable_predictions(_Analysis) -> []. design_prediction_tests(_Predictions, _State) -> []. optimize_hypothesis_test_design(_Designs, _State) -> #{}. create_hypothesis_test_execution_plan(_Design, _State) -> []. execute_hypothesis_tests(_Plan, _State) -> []. collect_hypothesis_observational_data(_Hypothesis, _State) -> []. combine_hypothesis_test_data(_Tests, _Observational) -> #{}. perform_statistical_analysis_of_results(_Results) -> #{}. assess_hypothesis_support(_Results, _Hypothesis, _Analysis) -> #{}. identify_alternative_explanations(_Results, _State) -> []. generate_followup_hypotheses(_Results, _Hypothesis, _State) -> []. calculate_hypothesis_conclusion_confidence(_Support, _Alternatives) -> 0.7. assess_overall_hypothesis_outcome(_Support, _Confidence) -> supported. update_hypothesis_tracking(_Id, _Analysis, Tracking) -> Tracking. map_conceptual_space(_Context, _State) -> #{}. identify_interesting_conceptual_regions(_Map, _State) -> []. select_conceptual_regions_for_exploration(_Regions, _State) -> []. explore_conceptual_regions(_Regions, _Context, _State) -> #{}. process_conceptual_exploration_results(_Results, _State) -> []. analyze_experiment_objective(_Objective, _Constraints, _State) -> #{}. generate_experiment_designs(_Analysis, _State) -> []. evaluate_experiment_designs(_Designs, _Constraints, _State) -> []. select_optimal_experiment_design(_Evaluated, _State) -> #{}. refine_experiment_design(_Design, _State) -> #{}. analyze_current_exploration_exploitation_balance(_State) -> #{exploration => 0.6, exploitation => 0.4}. determine_optimal_balance(_Context, _State) -> #{exploration => 0.5, exploitation => 0.5}. calculate_balance_adjustment(_Current, _Optimal) -> #{exploration_change => 0.1, exploitation_change => -0.1}. apply_exploration_exploitation_adjustment(_Adjustment, State) -> State. evaluate_ongoing_explorations(_State) -> []. identify_new_exploration_opportunities(_State) -> []. select_new_explorations(_Opportunities, _State) -> []. update_exploration_targets(_Evaluated, _Selected, Targets) -> Targets. update_exploration_history(_Evaluated, History) -> History. update_curiosity_level(_CuriosityState, _State) -> #curiosity_state{}. process_accumulated_surprises(_Accumulator) -> []. generate_new_interest_areas(_Curiosity, _Surprises, _State) -> []. update_areas_of_interest(_New, _Current) -> []. get_recent_discoveries(_State, _Count) -> []. analyze_discovery_patterns(_Discoveries, _State) -> #{}. identify_discovery_trends(_Discoveries, _State) -> #{}. update_exploration_preferences_from_discoveries(_Patterns, _Trends, Preferences) -> Preferences. generate_pattern_hypotheses_from_patterns(_Patterns, _State) -> [].