-module(environmental_learning_engine). -behaviour(gen_server). %% Environmental Learning and Adaptation Engine %% Advanced learning system that enables autonomous agents to: %% - Learn from environmental interactions and feedback %% - Build predictive models of environmental dynamics %% - Adapt behavior based on environmental changes %% - Discover environmental patterns and regularities %% - Form abstractions and generalizations about the environment %% - Transfer learning across similar environmental contexts %% - Maintain and update environmental mental models -export([start_link/1, % Core learning functions learn_from_interaction/3, adapt_to_environment/2, build_environmental_model/2, update_environmental_knowledge/3, predict_environmental_changes/2, % Pattern discovery and abstraction discover_environmental_patterns/2, form_environmental_abstractions/2, generalize_environmental_knowledge/2, extract_environmental_rules/2, % Adaptation and optimization optimize_environmental_strategy/2, adapt_behavioral_patterns/3, evolutionary_adaptation/2, reinforcement_learning_update/4, % Transfer learning transfer_knowledge_across_contexts/3, identify_similar_environments/2, abstract_environmental_features/2, contextualize_learning/3, % Environmental modeling build_predictive_model/2, update_causal_model/3, model_temporal_dynamics/2, simulate_environmental_scenarios/2, validate_environmental_model/3, % Curiosity and exploration learning curiosity_driven_learning/2, exploration_strategy_learning/2, novelty_detection_learning/2, surprise_based_learning/3, % Meta-learning learn_how_to_learn/2, adapt_learning_strategies/3, meta_cognitive_learning/2]). -export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]). %% Learning and adaptation data structures -record(environmental_experience, { experience_id, % Unique experience identifier context, % Environmental context action_taken, % Action that was taken environmental_state_before, % Environment state before action environmental_state_after, % Environment state after action outcome, % Outcome of the interaction feedback, % Environmental feedback received learning_value, % Value for learning (0-1) timestamp, % When experience occurred metadata = #{} % Additional metadata }). -record(environmental_pattern, { pattern_id, % Unique pattern identifier pattern_type, % Type of pattern (temporal, spatial, causal, etc.) pattern_description, % Description of the pattern pattern_conditions, % Conditions under which pattern holds pattern_confidence, % Confidence in pattern (0-1) supporting_evidence = [], % Evidence supporting this pattern counter_evidence = [], % Evidence against this pattern generalization_level = 1, % Level of generalization (1-10) applicability_scope, % Scope where pattern applies discovery_timestamp, % When pattern was discovered last_validation, % Last time pattern was validated usage_count = 0 % How often pattern has been used }). -record(adaptation_strategy, { strategy_id, % Unique strategy identifier strategy_type, % Type of adaptation strategy strategy_description, % Description of the strategy adaptation_parameters = #{}, % Parameters for adaptation effectiveness_history = [], % History of strategy effectiveness success_rate = 0.0, % Success rate of strategy (0-1) application_contexts = [], % Contexts where strategy applies learning_rate = 0.1, % Learning rate for this strategy exploration_vs_exploitation = 0.5, % Balance parameter (0-1) last_updated, % Last time strategy was updated adaptation_count = 0 % Number of times strategy adapted }). -record(environmental_model, { model_id, % Unique model identifier model_type, % Type of environmental model model_scope, % Scope of what the model covers state_variables = [], % Variables that define environmental state dynamic_equations = [], % Equations governing state transitions causal_relationships = #{}, % Causal relationships in environment temporal_patterns = [], % Temporal patterns in environment spatial_patterns = [], % Spatial patterns in environment uncertainty_estimates = #{}, % Uncertainty in different aspects model_accuracy = 0.0, % Measured accuracy of model prediction_history = [], % History of predictions made validation_results = [], % Results of model validation last_updated, % Last time model was updated confidence_level = 0.5 % Overall confidence in model }). -record(learning_state, { agent_id, % Associated agent environmental_experiences = [], % History of environmental experiences discovered_patterns = #{}, % Discovered environmental patterns adaptation_strategies = #{}, % Available adaptation strategies environmental_models = #{}, % Environmental models built learning_parameters = #{}, % Learning algorithm parameters curiosity_state = #{}, % Current curiosity and exploration state transfer_learning_memory = #{}, % Memory for transfer learning meta_learning_knowledge = #{}, % Knowledge about learning itself performance_metrics = #{}, % Learning performance metrics active_learning_processes = #{}, % Currently active learning processes environmental_surprises = [], % Recent environmental surprises adaptation_history = [] % History of adaptations made }). %%==================================================================== %% API functions %%==================================================================== start_link(Config) -> AgentId = maps:get(agent_id, Config, generate_learning_id()), io:format("[ENV_LEARNING] Starting environmental learning engine for agent ~p~n", [AgentId]), gen_server:start_link(?MODULE, [AgentId, Config], []). %% Core learning functions learn_from_interaction(LearningPid, Interaction, Outcome) -> gen_server:cast(LearningPid, {learn_from_interaction, Interaction, Outcome}). adapt_to_environment(LearningPid, EnvironmentalChange) -> gen_server:call(LearningPid, {adapt_to_environment, EnvironmentalChange}). build_environmental_model(LearningPid, EnvironmentalData) -> gen_server:call(LearningPid, {build_environmental_model, EnvironmentalData}). update_environmental_knowledge(LearningPid, NewKnowledge, Context) -> gen_server:cast(LearningPid, {update_environmental_knowledge, NewKnowledge, Context}). predict_environmental_changes(LearningPid, CurrentState) -> gen_server:call(LearningPid, {predict_environmental_changes, CurrentState}). %% Pattern discovery and abstraction discover_environmental_patterns(LearningPid, AnalysisScope) -> gen_server:call(LearningPid, {discover_environmental_patterns, AnalysisScope}). form_environmental_abstractions(LearningPid, ConcreteExperiences) -> gen_server:call(LearningPid, {form_environmental_abstractions, ConcreteExperiences}). generalize_environmental_knowledge(LearningPid, SpecificKnowledge) -> gen_server:call(LearningPid, {generalize_environmental_knowledge, SpecificKnowledge}). extract_environmental_rules(LearningPid, Observations) -> gen_server:call(LearningPid, {extract_environmental_rules, Observations}). %% Adaptation and optimization optimize_environmental_strategy(LearningPid, CurrentStrategy) -> gen_server:call(LearningPid, {optimize_environmental_strategy, CurrentStrategy}). adapt_behavioral_patterns(LearningPid, BehaviorPattern, Feedback) -> gen_server:call(LearningPid, {adapt_behavioral_patterns, BehaviorPattern, Feedback}). evolutionary_adaptation(LearningPid, SelectionPressure) -> gen_server:call(LearningPid, {evolutionary_adaptation, SelectionPressure}). reinforcement_learning_update(LearningPid, State, Action, Reward) -> gen_server:cast(LearningPid, {reinforcement_learning_update, State, Action, Reward}). %% Transfer learning transfer_knowledge_across_contexts(LearningPid, SourceContext, TargetContext) -> gen_server:call(LearningPid, {transfer_knowledge_across_contexts, SourceContext, TargetContext}). identify_similar_environments(LearningPid, CurrentEnvironment) -> gen_server:call(LearningPid, {identify_similar_environments, CurrentEnvironment}). abstract_environmental_features(LearningPid, EnvironmentalData) -> gen_server:call(LearningPid, {abstract_environmental_features, EnvironmentalData}). contextualize_learning(LearningPid, Learning, Context) -> gen_server:call(LearningPid, {contextualize_learning, Learning, Context}). %% Environmental modeling build_predictive_model(LearningPid, ModelType) -> gen_server:call(LearningPid, {build_predictive_model, ModelType}). update_causal_model(LearningPid, CausalData, ModelId) -> gen_server:call(LearningPid, {update_causal_model, CausalData, ModelId}). model_temporal_dynamics(LearningPid, TemporalData) -> gen_server:call(LearningPid, {model_temporal_dynamics, TemporalData}). simulate_environmental_scenarios(LearningPid, ScenarioParameters) -> gen_server:call(LearningPid, {simulate_environmental_scenarios, ScenarioParameters}). validate_environmental_model(LearningPid, ModelId, ValidationData) -> gen_server:call(LearningPid, {validate_environmental_model, ModelId, ValidationData}). %% Curiosity and exploration learning curiosity_driven_learning(LearningPid, CuriosityStimulus) -> gen_server:cast(LearningPid, {curiosity_driven_learning, CuriosityStimulus}). exploration_strategy_learning(LearningPid, ExplorationResults) -> gen_server:cast(LearningPid, {exploration_strategy_learning, ExplorationResults}). novelty_detection_learning(LearningPid, NovelStimulus) -> gen_server:cast(LearningPid, {novelty_detection_learning, NovelStimulus}). surprise_based_learning(LearningPid, ExpectedOutcome, ActualOutcome) -> gen_server:cast(LearningPid, {surprise_based_learning, ExpectedOutcome, ActualOutcome}). %% Meta-learning learn_how_to_learn(LearningPid, LearningExperience) -> gen_server:cast(LearningPid, {learn_how_to_learn, LearningExperience}). adapt_learning_strategies(LearningPid, PerformanceData, Context) -> gen_server:call(LearningPid, {adapt_learning_strategies, PerformanceData, Context}). meta_cognitive_learning(LearningPid, MetaCognitiveExperience) -> gen_server:cast(LearningPid, {meta_cognitive_learning, MetaCognitiveExperience}). %%==================================================================== %% gen_server callbacks %%==================================================================== init([AgentId, Config]) -> process_flag(trap_exit, true), io:format("[ENV_LEARNING] Initializing environmental learning engine for agent ~p~n", [AgentId]), % Initialize learning parameters LearningParams = initialize_learning_parameters(Config), % Initialize curiosity and exploration state CuriosityState = initialize_curiosity_state(Config), State = #learning_state{ agent_id = AgentId, learning_parameters = LearningParams, curiosity_state = CuriosityState }, % Start learning cycles schedule_pattern_discovery(), schedule_model_validation(), schedule_adaptation_evaluation(), {ok, State}. handle_call({adapt_to_environment, EnvironmentalChange}, _From, State) -> io:format("[ENV_LEARNING] Adapting to environmental change: ~p~n", [EnvironmentalChange]), % Analyze the environmental change ChangeAnalysis = analyze_environmental_change(EnvironmentalChange, State), % Select appropriate adaptation strategy AdaptationStrategy = select_adaptation_strategy(ChangeAnalysis, State), % Execute adaptation AdaptationResult = execute_adaptation_strategy(AdaptationStrategy, ChangeAnalysis, State), % Update adaptation history AdaptationRecord = create_adaptation_record(EnvironmentalChange, AdaptationStrategy, AdaptationResult), NewAdaptationHistory = [AdaptationRecord | State#learning_state.adaptation_history], % Update state NewState = State#learning_state{adaptation_history = NewAdaptationHistory}, {reply, {ok, AdaptationResult}, NewState}; handle_call({build_environmental_model, EnvironmentalData}, _From, State) -> io:format("[ENV_LEARNING] Building environmental model from data~n"), % Analyze environmental data DataAnalysis = analyze_environmental_data(EnvironmentalData, State), % Build model based on data characteristics ModelType = determine_model_type(DataAnalysis), NewModel = build_model_of_type(ModelType, EnvironmentalData, State), % Validate model ValidationResult = validate_model_internal(NewModel, EnvironmentalData), % Store model ModelId = NewModel#environmental_model.model_id, NewModels = maps:put(ModelId, NewModel, State#learning_state.environmental_models), NewState = State#learning_state{environmental_models = NewModels}, {reply, {ok, #{model_id => ModelId, validation => ValidationResult}}, NewState}; handle_call({predict_environmental_changes, CurrentState}, _From, State) -> io:format("[ENV_LEARNING] Predicting environmental changes from state: ~p~n", [CurrentState]), % Select best models for prediction RelevantModels = select_relevant_models(CurrentState, State), % Generate predictions using multiple models Predictions = generate_multi_model_predictions(CurrentState, RelevantModels, State), % Aggregate predictions AggregatedPrediction = aggregate_predictions(Predictions, State), % Estimate confidence in prediction PredictionConfidence = estimate_prediction_confidence(Predictions, State), Result = #{ predictions => Predictions, aggregated_prediction => AggregatedPrediction, confidence => PredictionConfidence, models_used => [M#environmental_model.model_id || M <- RelevantModels] }, {reply, {ok, Result}, State}; handle_call({discover_environmental_patterns, AnalysisScope}, _From, State) -> io:format("[ENV_LEARNING] Discovering environmental patterns in scope: ~p~n", [AnalysisScope]), % Get relevant experiences for analysis RelevantExperiences = filter_experiences_by_scope(AnalysisScope, State), % Apply pattern discovery algorithms DiscoveredPatterns = apply_pattern_discovery_algorithms(RelevantExperiences, State), % Validate discovered patterns ValidatedPatterns = validate_discovered_patterns(DiscoveredPatterns, State), % Store validated patterns NewPatterns = store_validated_patterns(ValidatedPatterns, State), UpdatedPatterns = maps:merge(State#learning_state.discovered_patterns, NewPatterns), NewState = State#learning_state{discovered_patterns = UpdatedPatterns}, {reply, {ok, ValidatedPatterns}, NewState}; handle_call({form_environmental_abstractions, ConcreteExperiences}, _From, State) -> io:format("[ENV_LEARNING] Forming environmental abstractions~n"), % Group similar experiences ExperienceGroups = group_similar_experiences(ConcreteExperiences, State), % Extract common features CommonFeatures = extract_common_features_from_groups(ExperienceGroups, State), % Form abstractions Abstractions = form_abstractions_from_features(CommonFeatures, State), % Validate abstractions ValidatedAbstractions = validate_abstractions(Abstractions, ConcreteExperiences, State), {reply, {ok, ValidatedAbstractions}, State}; handle_call({optimize_environmental_strategy, CurrentStrategy}, _From, State) -> io:format("[ENV_LEARNING] Optimizing environmental strategy: ~p~n", [CurrentStrategy]), % Analyze current strategy performance PerformanceAnalysis = analyze_strategy_performance(CurrentStrategy, State), % Identify optimization opportunities OptimizationOpportunities = identify_optimization_opportunities(PerformanceAnalysis, State), % Generate strategy variations StrategyVariations = generate_strategy_variations(CurrentStrategy, OptimizationOpportunities, State), % Evaluate strategy variations EvaluatedStrategies = evaluate_strategy_variations(StrategyVariations, State), % Select best strategy OptimizedStrategy = select_best_strategy(EvaluatedStrategies, State), {reply, {ok, OptimizedStrategy}, State}; handle_call({transfer_knowledge_across_contexts, SourceContext, TargetContext}, _From, State) -> io:format("[ENV_LEARNING] Transferring knowledge from ~p to ~p~n", [SourceContext, TargetContext]), % Analyze context similarity ContextSimilarity = analyze_context_similarity(SourceContext, TargetContext, State), % Extract transferable knowledge TransferableKnowledge = extract_transferable_knowledge(SourceContext, ContextSimilarity, State), % Adapt knowledge for target context AdaptedKnowledge = adapt_knowledge_for_context(TransferableKnowledge, TargetContext, State), % Validate transferred knowledge ValidationResults = validate_transferred_knowledge(AdaptedKnowledge, TargetContext, State), Result = #{ context_similarity => ContextSimilarity, transferable_knowledge => TransferableKnowledge, adapted_knowledge => AdaptedKnowledge, validation => ValidationResults }, {reply, {ok, Result}, State}; handle_call({build_predictive_model, ModelType}, _From, State) -> io:format("[ENV_LEARNING] Building predictive model of type: ~p~n", [ModelType]), % Collect relevant data for model ModelData = collect_model_data(ModelType, State), % Build model using appropriate algorithm Model = build_model_using_algorithm(ModelType, ModelData, State), % Train and validate model TrainedModel = train_model(Model, ModelData, State), ValidationResults = validate_model_performance(TrainedModel, ModelData, State), % Store model ModelId = TrainedModel#environmental_model.model_id, NewModels = maps:put(ModelId, TrainedModel, State#learning_state.environmental_models), NewState = State#learning_state{environmental_models = NewModels}, Result = #{ model_id => ModelId, model_type => ModelType, validation_results => ValidationResults }, {reply, {ok, Result}, NewState}; handle_call({adapt_learning_strategies, PerformanceData, Context}, _From, State) -> io:format("[ENV_LEARNING] Adapting learning strategies based on performance~n"), % Analyze learning performance PerformanceAnalysis = analyze_learning_performance(PerformanceData, Context, State), % Identify learning strategy improvements StrategyImprovements = identify_learning_strategy_improvements(PerformanceAnalysis, State), % Adapt learning parameters AdaptedParameters = adapt_learning_parameters(StrategyImprovements, State), % Update learning strategies UpdatedStrategies = update_learning_strategies(AdaptedParameters, State), NewState = State#learning_state{ learning_parameters = AdaptedParameters, adaptation_strategies = UpdatedStrategies }, {reply, {ok, #{adapted_parameters => AdaptedParameters, updated_strategies => maps:keys(UpdatedStrategies)}}, NewState}; handle_call(_Request, _From, State) -> {reply, {error, unknown_request}, State}. handle_cast({learn_from_interaction, Interaction, Outcome}, State) -> io:format("[ENV_LEARNING] Learning from interaction: ~p -> ~p~n", [Interaction, Outcome]), % Create environmental experience record Experience = create_environmental_experience(Interaction, Outcome), % Extract learning value from experience LearningValue = calculate_learning_value(Experience, State), UpdatedExperience = Experience#environmental_experience{learning_value = LearningValue}, % Add to experience history NewExperiences = [UpdatedExperience | State#learning_state.environmental_experiences], % Update patterns based on new experience UpdatedPatterns = update_patterns_from_experience(UpdatedExperience, State#learning_state.discovered_patterns), % Update models based on new experience UpdatedModels = update_models_from_experience(UpdatedExperience, State#learning_state.environmental_models), NewState = State#learning_state{ environmental_experiences = NewExperiences, discovered_patterns = UpdatedPatterns, environmental_models = UpdatedModels }, {noreply, NewState}; handle_cast({curiosity_driven_learning, CuriosityStimulus}, State) -> io:format("[ENV_LEARNING] Curiosity-driven learning from stimulus: ~p~n", [CuriosityStimulus]), % Analyze novelty of stimulus NoveltyAnalysis = analyze_stimulus_novelty(CuriosityStimulus, State), % Generate curiosity-driven learning goals _CuriosityGoals = generate_curiosity_learning_goals(NoveltyAnalysis, State), % Update curiosity state NewCuriosityState = update_curiosity_state(CuriosityStimulus, NoveltyAnalysis, State#learning_state.curiosity_state), NewState = State#learning_state{curiosity_state = NewCuriosityState}, {noreply, NewState}; handle_cast({surprise_based_learning, ExpectedOutcome, ActualOutcome}, State) -> io:format("[ENV_LEARNING] Surprise-based learning: expected ~p, got ~p~n", [ExpectedOutcome, ActualOutcome]), % Calculate surprise magnitude SurpriseMagnitude = calculate_surprise_magnitude(ExpectedOutcome, ActualOutcome), % Create surprise record SurpriseRecord = #{ expected => ExpectedOutcome, actual => ActualOutcome, magnitude => SurpriseMagnitude, timestamp => erlang:system_time(second) }, % Update model confidence based on surprise UpdatedModels = update_model_confidence_from_surprise(SurpriseRecord, State#learning_state.environmental_models), % Add to surprise history NewSurprises = [SurpriseRecord | State#learning_state.environmental_surprises], NewState = State#learning_state{ environmental_models = UpdatedModels, environmental_surprises = NewSurprises }, {noreply, NewState}; handle_cast({reinforcement_learning_update, StateData, Action, Reward}, State) -> io:format("[ENV_LEARNING] Reinforcement learning update: ~p -> ~p (reward: ~p)~n", [StateData, Action, Reward]), % Update value functions UpdatedStrategies = update_rl_strategies(StateData, Action, Reward, State#learning_state.adaptation_strategies), NewState = State#learning_state{adaptation_strategies = UpdatedStrategies}, {noreply, NewState}; handle_cast(_Msg, State) -> {noreply, State}. handle_info(pattern_discovery_cycle, State) -> % Periodic pattern discovery NewState = perform_periodic_pattern_discovery(State), schedule_pattern_discovery(), {noreply, NewState}; handle_info(model_validation_cycle, State) -> % Periodic model validation NewState = perform_periodic_model_validation(State), schedule_model_validation(), {noreply, NewState}; handle_info(adaptation_evaluation_cycle, State) -> % Periodic adaptation evaluation NewState = perform_periodic_adaptation_evaluation(State), schedule_adaptation_evaluation(), {noreply, NewState}; handle_info(_Info, State) -> {noreply, State}. terminate(_Reason, State) -> io:format("[ENV_LEARNING] Environmental learning engine for agent ~p terminating~n", [State#learning_state.agent_id]), save_learning_state(State), ok. code_change(_OldVsn, State, _Extra) -> {ok, State}. %%==================================================================== %% Internal functions - Learning and Adaptation %%==================================================================== analyze_environmental_change(EnvironmentalChange, State) -> % Analyze the characteristics of an environmental change % Determine change magnitude ChangeMagnitude = calculate_change_magnitude(EnvironmentalChange), % Determine change type ChangeType = classify_change_type(EnvironmentalChange), % Assess change predictability ChangePredictability = assess_change_predictability(EnvironmentalChange, State), % Identify affected domains AffectedDomains = identify_affected_domains(EnvironmentalChange, State), #{ magnitude => ChangeMagnitude, type => ChangeType, predictability => ChangePredictability, affected_domains => AffectedDomains, analysis_timestamp => erlang:system_time(second) }. select_adaptation_strategy(ChangeAnalysis, State) -> % Select appropriate adaptation strategy based on change analysis AvailableStrategies = maps:values(State#learning_state.adaptation_strategies), % Score strategies for this type of change StrategyScores = score_strategies_for_change(ChangeAnalysis, AvailableStrategies), % Select best strategy BestStrategy = select_highest_scoring_strategy(StrategyScores), BestStrategy. execute_adaptation_strategy(Strategy, ChangeAnalysis, State) -> % Execute the selected adaptation strategy case Strategy#adaptation_strategy.strategy_type of reactive -> execute_reactive_adaptation(Strategy, ChangeAnalysis, State); proactive -> execute_proactive_adaptation(Strategy, ChangeAnalysis, State); evolutionary -> execute_evolutionary_adaptation_internal(Strategy, ChangeAnalysis, State); learning_based -> execute_learning_based_adaptation(Strategy, ChangeAnalysis, State); _ -> execute_general_adaptation(Strategy, ChangeAnalysis, State) end. create_environmental_experience(Interaction, Outcome) -> #environmental_experience{ experience_id = generate_experience_id(), context = maps:get(context, Interaction, #{}), action_taken = maps:get(action, Interaction, undefined), environmental_state_before = maps:get(state_before, Interaction, #{}), environmental_state_after = maps:get(state_after, Outcome, #{}), outcome = Outcome, feedback = maps:get(feedback, Outcome, #{}), timestamp = erlang:system_time(second) }. calculate_learning_value(Experience, State) -> % Calculate the learning value of an experience % Factor 1: Novelty of the experience NoveltyValue = calculate_experience_novelty(Experience, State), % Factor 2: Surprise level SurpriseValue = calculate_experience_surprise(Experience, State), % Factor 3: Relevance to current goals RelevanceValue = calculate_experience_relevance(Experience, State), % Factor 4: Potential for generalization GeneralizationValue = calculate_generalization_potential(Experience, State), % Combine factors TotalValue = (NoveltyValue + SurpriseValue + RelevanceValue + GeneralizationValue) / 4, max(0.0, min(1.0, TotalValue)). %%==================================================================== %% Internal functions - Pattern Discovery %%==================================================================== apply_pattern_discovery_algorithms(Experiences, State) -> % Apply various pattern discovery algorithms % Temporal pattern discovery TemporalPatterns = discover_temporal_patterns(Experiences, State), % Sequential pattern discovery SequentialPatterns = discover_sequential_patterns(Experiences, State), % Causal pattern discovery CausalPatterns = discover_causal_patterns(Experiences, State), % Statistical pattern discovery StatisticalPatterns = discover_statistical_patterns(Experiences, State), % Combine all discovered patterns AllPatterns = TemporalPatterns ++ SequentialPatterns ++ CausalPatterns ++ StatisticalPatterns, AllPatterns. discover_temporal_patterns(Experiences, _State) -> % Discover patterns in temporal sequences % Sort experiences by timestamp SortedExperiences = lists:sort(fun(E1, E2) -> E1#environmental_experience.timestamp =< E2#environmental_experience.timestamp end, Experiences), % Look for recurring temporal sequences TemporalSequences = extract_temporal_sequences(SortedExperiences), % Convert sequences to patterns TemporalPatterns = convert_sequences_to_patterns(TemporalSequences, temporal), TemporalPatterns. discover_causal_patterns(Experiences, _State) -> % Discover causal patterns in experiences % Group experiences by similar contexts ContextGroups = group_experiences_by_context(Experiences), % For each group, look for causal relationships CausalRelationships = lists:flatmap(fun(Group) -> find_causal_relationships_in_group(Group) end, ContextGroups), % Convert causal relationships to patterns CausalPatterns = convert_causal_relationships_to_patterns(CausalRelationships), CausalPatterns. validate_discovered_patterns(Patterns, State) -> % Validate discovered patterns against historical data ValidationResults = lists:map(fun(Pattern) -> ValidationResult = validate_pattern_against_history(Pattern, State), {Pattern, ValidationResult} end, Patterns), % Keep only patterns that pass validation ValidatedPatterns = [Pattern || {Pattern, {valid, _}} <- ValidationResults], ValidatedPatterns. %%==================================================================== %% Internal functions - Environmental Modeling %%==================================================================== determine_model_type(DataAnalysis) -> % Determine appropriate model type based on data characteristics DataCharacteristics = maps:get(characteristics, DataAnalysis, #{}), % Check for temporal dependencies HasTemporalDependency = maps:get(temporal_dependency, DataCharacteristics, false), % Check for causal relationships HasCausalRelationships = maps:get(causal_relationships, DataCharacteristics, false), % Check for stochastic elements HasStochasticElements = maps:get(stochastic_elements, DataCharacteristics, false), % Select model type based on characteristics if HasTemporalDependency and HasCausalRelationships -> dynamic_causal_model; HasTemporalDependency -> temporal_model; HasCausalRelationships -> causal_model; HasStochasticElements -> probabilistic_model; true -> statistical_model end. build_model_of_type(ModelType, Data, State) -> % Build environmental model of specified type ModelId = generate_model_id(), BaseModel = #environmental_model{ model_id = ModelId, model_type = ModelType, last_updated = erlang:system_time(second) }, % Build model based on type case ModelType of dynamic_causal_model -> build_dynamic_causal_model(BaseModel, Data, State); temporal_model -> build_temporal_model(BaseModel, Data, State); causal_model -> build_causal_model(BaseModel, Data, State); probabilistic_model -> build_probabilistic_model(BaseModel, Data, State); statistical_model -> build_statistical_model(BaseModel, Data, State); _ -> build_general_model(BaseModel, Data, State) end. select_relevant_models(CurrentState, State) -> % Select models that are relevant for predicting from current state AllModels = maps:values(State#learning_state.environmental_models), % Filter models by relevance RelevantModels = lists:filter(fun(Model) -> is_model_relevant_for_state(Model, CurrentState) end, AllModels), % Sort by model accuracy SortedModels = lists:sort(fun(M1, M2) -> M1#environmental_model.model_accuracy >= M2#environmental_model.model_accuracy end, RelevantModels), SortedModels. generate_multi_model_predictions(CurrentState, Models, State) -> % Generate predictions using multiple models Predictions = lists:map(fun(Model) -> Prediction = generate_model_prediction(Model, CurrentState, State), {Model#environmental_model.model_id, Prediction} end, Models), Predictions. %%==================================================================== %% Internal functions - Transfer Learning %%==================================================================== analyze_context_similarity(SourceContext, TargetContext, State) -> % Analyze similarity between two contexts % Extract features from both contexts SourceFeatures = extract_context_features(SourceContext, State), TargetFeatures = extract_context_features(TargetContext, State), % Calculate feature similarity FeatureSimilarity = calculate_feature_similarity(SourceFeatures, TargetFeatures), % Calculate structural similarity StructuralSimilarity = calculate_structural_similarity(SourceContext, TargetContext), % Calculate functional similarity FunctionalSimilarity = calculate_functional_similarity(SourceContext, TargetContext, State), #{ feature_similarity => FeatureSimilarity, structural_similarity => StructuralSimilarity, functional_similarity => FunctionalSimilarity, overall_similarity => (FeatureSimilarity + StructuralSimilarity + FunctionalSimilarity) / 3 }. extract_transferable_knowledge(SourceContext, ContextSimilarity, State) -> % Extract knowledge that can be transferred between contexts SimilarityThreshold = 0.6, OverallSimilarity = maps:get(overall_similarity, ContextSimilarity), if OverallSimilarity >= SimilarityThreshold -> % High similarity - transfer detailed knowledge extract_detailed_transferable_knowledge(SourceContext, State); true -> % Low similarity - transfer only abstract knowledge extract_abstract_transferable_knowledge(SourceContext, State) end. %%==================================================================== %% Internal functions - Curiosity and Exploration %%==================================================================== analyze_stimulus_novelty(Stimulus, State) -> % Analyze how novel a stimulus is % Compare with past experiences SimilarExperiences = find_similar_experiences(Stimulus, State), % Calculate novelty based on similarity NoveltyScore = calculate_novelty_score(Stimulus, SimilarExperiences), % Analyze specific novelty dimensions FeatureNovelty = analyze_feature_novelty(Stimulus, State), StructuralNovelty = analyze_structural_novelty(Stimulus, State), ContextualNovelty = analyze_contextual_novelty(Stimulus, State), #{ overall_novelty => NoveltyScore, feature_novelty => FeatureNovelty, structural_novelty => StructuralNovelty, contextual_novelty => ContextualNovelty, similar_experiences_count => length(SimilarExperiences) }. generate_curiosity_learning_goals(NoveltyAnalysis, State) -> % Generate learning goals based on novelty analysis NoveltyScore = maps:get(overall_novelty, NoveltyAnalysis), if NoveltyScore > 0.8 -> % High novelty - explore extensively generate_extensive_exploration_goals(NoveltyAnalysis, State); NoveltyScore > 0.5 -> % Medium novelty - targeted exploration generate_targeted_exploration_goals(NoveltyAnalysis, State); true -> % Low novelty - minimal exploration generate_minimal_exploration_goals(NoveltyAnalysis, State) end. %%==================================================================== %% Internal functions - Utility and Helper Functions %%==================================================================== initialize_learning_parameters(Config) -> #{ learning_rate => maps:get(learning_rate, Config, 0.1), exploration_rate => maps:get(exploration_rate, Config, 0.2), adaptation_threshold => maps:get(adaptation_threshold, Config, 0.7), pattern_discovery_sensitivity => maps:get(pattern_discovery_sensitivity, Config, 0.6), transfer_learning_threshold => maps:get(transfer_learning_threshold, Config, 0.5), curiosity_drive => maps:get(curiosity_drive, Config, 0.3), novelty_seeking => maps:get(novelty_seeking, Config, 0.4) }. initialize_curiosity_state(Config) -> #{ current_curiosity_level => maps:get(initial_curiosity, Config, 0.5), exploration_history => [], novelty_memory => [], interest_areas => [], boredom_threshold => maps:get(boredom_threshold, Config, 0.3) }. schedule_pattern_discovery() -> Interval = 120000, % 2 minutes erlang:send_after(Interval, self(), pattern_discovery_cycle). schedule_model_validation() -> Interval = 300000, % 5 minutes erlang:send_after(Interval, self(), model_validation_cycle). schedule_adaptation_evaluation() -> Interval = 180000, % 3 minutes erlang:send_after(Interval, self(), adaptation_evaluation_cycle). perform_periodic_pattern_discovery(State) -> % Perform periodic pattern discovery on recent experiences RecentExperiences = get_recent_experiences(State, 100), % Last 100 experiences if length(RecentExperiences) >= 10 -> DiscoveredPatterns = apply_pattern_discovery_algorithms(RecentExperiences, State), ValidatedPatterns = validate_discovered_patterns(DiscoveredPatterns, State), NewPatterns = store_validated_patterns(ValidatedPatterns, State), UpdatedPatterns = maps:merge(State#learning_state.discovered_patterns, NewPatterns), State#learning_state{discovered_patterns = UpdatedPatterns}; true -> State end. perform_periodic_model_validation(State) -> % Validate existing models against recent data RecentExperiences = get_recent_experiences(State, 50), if length(RecentExperiences) >= 10 -> UpdatedModels = maps:map(fun(_ModelId, Model) -> ValidationResult = validate_model_against_experiences(Model, RecentExperiences), update_model_confidence(Model, ValidationResult) end, State#learning_state.environmental_models), State#learning_state{environmental_models = UpdatedModels}; true -> State end. perform_periodic_adaptation_evaluation(State) -> % Evaluate the effectiveness of recent adaptations RecentAdaptations = get_recent_adaptations(State, 10), EvaluationResults = lists:map(fun(Adaptation) -> evaluate_adaptation_effectiveness(Adaptation, State) end, RecentAdaptations), % Update adaptation strategies based on evaluations UpdatedStrategies = update_strategies_from_evaluations(EvaluationResults, State#learning_state.adaptation_strategies), State#learning_state{adaptation_strategies = UpdatedStrategies}. generate_learning_id() -> iolist_to_binary(io_lib:format("env_learning_~p", [erlang:system_time(microsecond)])). generate_experience_id() -> iolist_to_binary(io_lib:format("experience_~p", [erlang:system_time(microsecond)])). generate_model_id() -> iolist_to_binary(io_lib:format("env_model_~p", [erlang:system_time(microsecond)])). save_learning_state(_State) -> % Save learning state to persistent storage ok. % Placeholder implementations for complex functions calculate_change_magnitude(_Change) -> 0.5. classify_change_type(_Change) -> gradual. assess_change_predictability(_Change, _State) -> 0.6. identify_affected_domains(_Change, _State) -> [general]. score_strategies_for_change(_Analysis, Strategies) -> [{S, 0.5} || S <- Strategies]. select_highest_scoring_strategy(StrategyScores) -> element(1, hd(StrategyScores)). execute_reactive_adaptation(_Strategy, _Analysis, _State) -> #{type => reactive}. execute_proactive_adaptation(_Strategy, _Analysis, _State) -> #{type => proactive}. execute_evolutionary_adaptation_internal(_Strategy, _Analysis, _State) -> #{type => evolutionary}. execute_learning_based_adaptation(_Strategy, _Analysis, _State) -> #{type => learning_based}. execute_general_adaptation(_Strategy, _Analysis, _State) -> #{type => general}. create_adaptation_record(Change, Strategy, Result) -> #{change => Change, strategy => Strategy, result => Result}. calculate_experience_novelty(_Experience, _State) -> 0.5. calculate_experience_surprise(_Experience, _State) -> 0.4. calculate_experience_relevance(_Experience, _State) -> 0.6. calculate_generalization_potential(_Experience, _State) -> 0.5. extract_temporal_sequences(_Experiences) -> []. convert_sequences_to_patterns(_Sequences, _Type) -> []. group_experiences_by_context(_Experiences) -> []. find_causal_relationships_in_group(_Group) -> []. convert_causal_relationships_to_patterns(_Relationships) -> []. validate_pattern_against_history(_Pattern, _State) -> {valid, 0.8}. store_validated_patterns(Patterns, _State) -> maps:from_list([{P, P} || P <- Patterns]). analyze_environmental_data(_Data, _State) -> #{characteristics => #{}}. build_dynamic_causal_model(Model, _Data, _State) -> Model. build_temporal_model(Model, _Data, _State) -> Model. build_causal_model(Model, _Data, _State) -> Model. build_probabilistic_model(Model, _Data, _State) -> Model. build_statistical_model(Model, _Data, _State) -> Model. build_general_model(Model, _Data, _State) -> Model. validate_model_internal(_Model, _Data) -> #{accuracy => 0.8}. is_model_relevant_for_state(_Model, _State) -> true. generate_model_prediction(_Model, _State, _LearningState) -> #{prediction => example}. aggregate_predictions(Predictions, _State) -> #{aggregated => Predictions}. estimate_prediction_confidence(_Predictions, _State) -> 0.7. filter_experiences_by_scope(_Scope, State) -> State#learning_state.environmental_experiences. group_similar_experiences(_Experiences, _State) -> []. extract_common_features_from_groups(_Groups, _State) -> []. form_abstractions_from_features(_Features, _State) -> []. validate_abstractions(_Abstractions, _Experiences, _State) -> []. analyze_strategy_performance(_Strategy, _State) -> #{performance => 0.7}. identify_optimization_opportunities(_Analysis, _State) -> []. generate_strategy_variations(_Strategy, _Opportunities, _State) -> []. evaluate_strategy_variations(_Variations, _State) -> []. select_best_strategy(_Evaluated, _State) -> #{}. extract_context_features(_Context, _State) -> []. calculate_feature_similarity(_Features1, _Features2) -> 0.6. calculate_structural_similarity(_Context1, _Context2) -> 0.5. calculate_functional_similarity(_Context1, _Context2, _State) -> 0.7. extract_detailed_transferable_knowledge(_Context, _State) -> #{}. extract_abstract_transferable_knowledge(_Context, _State) -> #{}. adapt_knowledge_for_context(_Knowledge, _Context, _State) -> #{}. validate_transferred_knowledge(_Knowledge, _Context, _State) -> #{valid => true}. find_similar_experiences(_Stimulus, _State) -> []. calculate_novelty_score(_Stimulus, _Similar) -> 0.6. analyze_feature_novelty(_Stimulus, _State) -> 0.5. analyze_structural_novelty(_Stimulus, _State) -> 0.4. analyze_contextual_novelty(_Stimulus, _State) -> 0.7. generate_extensive_exploration_goals(_Analysis, _State) -> []. generate_targeted_exploration_goals(_Analysis, _State) -> []. generate_minimal_exploration_goals(_Analysis, _State) -> []. update_patterns_from_experience(_Experience, Patterns) -> Patterns. update_models_from_experience(_Experience, Models) -> Models. update_curiosity_state(_Stimulus, _Analysis, CuriosityState) -> CuriosityState. calculate_surprise_magnitude(_Expected, _Actual) -> 0.5. update_model_confidence_from_surprise(_Surprise, Models) -> Models. update_rl_strategies(_State, _Action, _Reward, Strategies) -> Strategies. collect_model_data(_ModelType, _State) -> []. build_model_using_algorithm(_ModelType, _Data, _State) -> #{model_id => generate_model_id(), model_type => basic, data => []}. train_model(Model, _Data, _State) -> Model. validate_model_performance(_Model, _Data, _State) -> #{accuracy => 0.8}. analyze_learning_performance(_Data, _Context, _State) -> #{}. discover_sequential_patterns(_Experiences, _State) -> []. discover_statistical_patterns(_Experiences, _State) -> []. identify_learning_strategy_improvements(_Analysis, _State) -> []. adapt_learning_parameters(_Improvements, State) -> State#learning_state.learning_parameters. update_learning_strategies(_Parameters, State) -> State#learning_state.adaptation_strategies. get_recent_experiences(State, Count) -> lists:sublist(State#learning_state.environmental_experiences, Count). get_recent_adaptations(State, Count) -> lists:sublist(State#learning_state.adaptation_history, Count). validate_model_against_experiences(_Model, _Experiences) -> #{accuracy => 0.7}. update_model_confidence(Model, _ValidationResult) -> Model. evaluate_adaptation_effectiveness(_Adaptation, _State) -> #{effectiveness => 0.8}. update_strategies_from_evaluations(_Evaluations, Strategies) -> Strategies.