agents

-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.