-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) -> [].
