agents

-module(deep_reasoning_engine).
-behaviour(gen_server).

%% Deep Reasoning and Meta-Cognitive Engine
%% Sophisticated reasoning system that provides multiple levels of cognitive processing:
%% - Multi-level reasoning (reactive, deliberative, reflective)
%% - Meta-cognitive awareness and control
%% - Causal reasoning and inference
%% - Counterfactual and hypothetical thinking
%% - Analogical and metaphorical reasoning
%% - Abductive inference and explanation generation
%% - Higher-order cognitive processes
%% - Consciousness simulation and self-awareness

-export([start_link/1,
         % Core reasoning functions
         reason/3, multi_level_reason/3, meta_reason/2,
         causal_inference/3, counterfactual_reasoning/3, analogical_reasoning/4,
         abductive_inference/2, explanatory_reasoning/3,
         % Meta-cognitive functions
         metacognitive_monitoring/1, metacognitive_control/2, cognitive_strategy_selection/2,
         self_assessment/1, cognitive_load_monitoring/1, attention_control/2,
         consciousness_simulation/1, self_awareness_analysis/1,
         % Advanced reasoning
         hypothetical_reasoning/3, modal_reasoning/3, temporal_reasoning/3,
         probabilistic_reasoning/3, fuzzy_reasoning/3, dialectical_reasoning/3,
         creative_reasoning/2, intuitive_reasoning/2,
         % Reasoning analysis
         analyze_reasoning_process/2, evaluate_reasoning_quality/2,
         trace_reasoning_steps/2, explain_reasoning/2,
         % Cognitive control
         set_reasoning_mode/2, adjust_cognitive_parameters/2,
         get_reasoning_state/1, reset_reasoning_context/1]).

-export([init/1, handle_call/3, handle_cast/2, handle_info/2, 
         terminate/2, code_change/3]).

%% Reasoning structures and cognitive models
-record(reasoning_context, {
    current_problem,                    % Current problem being reasoned about
    reasoning_mode = deliberative,      % Current reasoning mode
    cognitive_load = 0.5,              % Current cognitive load (0-1)
    attention_focus = [],               % Current attention focus
    working_memory = [],                % Current working memory contents
    reasoning_depth = 3,                % Depth of reasoning (1-10)
    confidence_threshold = 0.7,         % Minimum confidence for conclusions
    time_constraints = infinity,        % Time constraints for reasoning
    resource_constraints = #{},         % Available cognitive resources
    meta_level = 1                     % Current meta-cognitive level
}).

-record(reasoning_step, {
    step_id,                           % Unique step identifier
    step_type,                         % Type of reasoning step
    inputs,                            % Input information/premises
    process,                           % Reasoning process applied
    outputs,                           % Outputs/conclusions
    confidence,                        % Confidence in this step
    justification,                     % Justification for this step
    meta_info = #{},                   % Meta-information about step
    timestamp                          % When step was performed
}).

-record(causal_model, {
    cause_variables = [],              % Identified causal variables
    effect_variables = [],             % Identified effect variables
    causal_relationships = #{},        % Causal relationship mappings
    confounding_factors = [],          % Known confounding factors
    causal_strength = #{},             % Strength of causal relationships
    temporal_constraints = [],         % Temporal ordering constraints
    intervention_effects = #{},        % Effects of hypothetical interventions
    confidence_levels = #{}            % Confidence in causal claims
}).

-record(counterfactual_scenario, {
    scenario_id,                       % Unique scenario identifier
    actual_world,                      % Description of actual world
    counterfactual_world,              % Description of counterfactual world
    intervention_point,                % Point where intervention occurs
    divergence_analysis,               % Analysis of how worlds diverge
    outcome_comparison,                % Comparison of outcomes
    plausibility_score,                % How plausible the counterfactual is
    implications = []                  % Implications of the counterfactual
}).

-record(meta_cognitive_state, {
    self_awareness_level = 0.5,        % Level of self-awareness (0-1)
    cognitive_monitoring = #{},        % Monitoring of cognitive processes
    cognitive_control_actions = [],    % Active cognitive control actions
    strategy_effectiveness = #{},      % Effectiveness of reasoning strategies
    meta_knowledge = #{},              % Knowledge about own cognitive processes
    cognitive_biases_detected = [],    % Detected cognitive biases
    reasoning_confidence = 0.7,        % Confidence in own reasoning
    learning_from_mistakes = []        % Learning from reasoning errors
}).

-record(reasoning_state, {
    agent_id,                          % Associated agent
    reasoning_context = #reasoning_context{}, % Current reasoning context
    active_reasoning_processes = #{},  % Currently active reasoning processes
    reasoning_history = [],            % History of reasoning episodes
    causal_models = #{},               % Domain-specific causal models
    meta_cognitive_state = #meta_cognitive_state{}, % Meta-cognitive state
    reasoning_strategies = [],         % Available reasoning strategies
    cognitive_resources = #{},         % Available cognitive resources
    reasoning_cache = #{},             % Cache for reasoning results
    performance_metrics = #{}          % Performance tracking metrics
}).

%%====================================================================
%% API functions
%%====================================================================

start_link(Config) ->
    AgentId = maps:get(agent_id, Config, generate_reasoning_id()),
    io:format("[REASONING] Starting deep reasoning engine for agent ~p~n", [AgentId]),
    gen_server:start_link(?MODULE, [AgentId, Config], []).

%% Core reasoning functions
reason(ReasoningPid, Problem, Context) ->
    gen_server:call(ReasoningPid, {reason, Problem, Context}).

multi_level_reason(ReasoningPid, Problem, Levels) ->
    gen_server:call(ReasoningPid, {multi_level_reason, Problem, Levels}).

meta_reason(ReasoningPid, ReasoningProcess) ->
    gen_server:call(ReasoningPid, {meta_reason, ReasoningProcess}).

causal_inference(ReasoningPid, CauseData, EffectData) ->
    gen_server:call(ReasoningPid, {causal_inference, CauseData, EffectData}).

counterfactual_reasoning(ReasoningPid, ActualWorld, Intervention) ->
    gen_server:call(ReasoningPid, {counterfactual_reasoning, ActualWorld, Intervention}).

analogical_reasoning(ReasoningPid, SourceDomain, TargetDomain, MappingConstraints) ->
    gen_server:call(ReasoningPid, {analogical_reasoning, SourceDomain, TargetDomain, MappingConstraints}).

abductive_inference(ReasoningPid, Observations) ->
    gen_server:call(ReasoningPid, {abductive_inference, Observations}).

explanatory_reasoning(ReasoningPid, Phenomenon, ExplanationCriteria) ->
    gen_server:call(ReasoningPid, {explanatory_reasoning, Phenomenon, ExplanationCriteria}).

%% Meta-cognitive functions
metacognitive_monitoring(ReasoningPid) ->
    gen_server:call(ReasoningPid, metacognitive_monitoring).

metacognitive_control(ReasoningPid, ControlAction) ->
    gen_server:call(ReasoningPid, {metacognitive_control, ControlAction}).

cognitive_strategy_selection(ReasoningPid, Problem) ->
    gen_server:call(ReasoningPid, {cognitive_strategy_selection, Problem}).

self_assessment(ReasoningPid) ->
    gen_server:call(ReasoningPid, self_assessment).

cognitive_load_monitoring(ReasoningPid) ->
    gen_server:call(ReasoningPid, cognitive_load_monitoring).

attention_control(ReasoningPid, AttentionDirective) ->
    gen_server:call(ReasoningPid, {attention_control, AttentionDirective}).

consciousness_simulation(ReasoningPid) ->
    gen_server:call(ReasoningPid, consciousness_simulation).

self_awareness_analysis(ReasoningPid) ->
    gen_server:call(ReasoningPid, self_awareness_analysis).

%% Advanced reasoning
hypothetical_reasoning(ReasoningPid, Hypothesis, TestConditions) ->
    gen_server:call(ReasoningPid, {hypothetical_reasoning, Hypothesis, TestConditions}).

modal_reasoning(ReasoningPid, ModalType, Proposition) ->
    gen_server:call(ReasoningPid, {modal_reasoning, ModalType, Proposition}).

temporal_reasoning(ReasoningPid, TemporalEvents, TimeConstraints) ->
    gen_server:call(ReasoningPid, {temporal_reasoning, TemporalEvents, TimeConstraints}).

probabilistic_reasoning(ReasoningPid, ProbabilisticData, InferenceType) ->
    gen_server:call(ReasoningPid, {probabilistic_reasoning, ProbabilisticData, InferenceType}).

fuzzy_reasoning(ReasoningPid, FuzzyData, FuzzyRules) ->
    gen_server:call(ReasoningPid, {fuzzy_reasoning, FuzzyData, FuzzyRules}).

dialectical_reasoning(ReasoningPid, Thesis, Antithesis) ->
    gen_server:call(ReasoningPid, {dialectical_reasoning, Thesis, Antithesis}).

creative_reasoning(ReasoningPid, CreativeChallenge) ->
    gen_server:call(ReasoningPid, {creative_reasoning, CreativeChallenge}).

intuitive_reasoning(ReasoningPid, IntuitiveInput) ->
    gen_server:call(ReasoningPid, {intuitive_reasoning, IntuitiveInput}).

%% Reasoning analysis
analyze_reasoning_process(ReasoningPid, ReasoningTrace) ->
    gen_server:call(ReasoningPid, {analyze_reasoning_process, ReasoningTrace}).

evaluate_reasoning_quality(ReasoningPid, ReasoningResult) ->
    gen_server:call(ReasoningPid, {evaluate_reasoning_quality, ReasoningResult}).

trace_reasoning_steps(ReasoningPid, ReasoningProcess) ->
    gen_server:call(ReasoningPid, {trace_reasoning_steps, ReasoningProcess}).

explain_reasoning(ReasoningPid, ReasoningConclusion) ->
    gen_server:call(ReasoningPid, {explain_reasoning, ReasoningConclusion}).

%% Cognitive control
set_reasoning_mode(ReasoningPid, Mode) ->
    gen_server:call(ReasoningPid, {set_reasoning_mode, Mode}).

adjust_cognitive_parameters(ReasoningPid, Parameters) ->
    gen_server:call(ReasoningPid, {adjust_cognitive_parameters, Parameters}).

get_reasoning_state(ReasoningPid) ->
    gen_server:call(ReasoningPid, get_reasoning_state).

reset_reasoning_context(ReasoningPid) ->
    gen_server:call(ReasoningPid, reset_reasoning_context).

%%====================================================================
%% gen_server callbacks
%%====================================================================

init([AgentId, Config]) ->
    process_flag(trap_exit, true),
    
    io:format("[REASONING] Initializing deep reasoning engine for agent ~p~n", [AgentId]),
    
    % Initialize reasoning strategies
    Strategies = initialize_reasoning_strategies(Config),
    
    % Initialize cognitive resources
    CognitiveResources = initialize_cognitive_resources(Config),
    
    State = #reasoning_state{
        agent_id = AgentId,
        reasoning_strategies = Strategies,
        cognitive_resources = CognitiveResources
    },
    
    % Start cognitive monitoring cycle
    schedule_cognitive_monitoring(),
    
    {ok, State}.

handle_call({reason, Problem, Context}, _From, State) ->
    io:format("[REASONING] Reasoning about problem: ~p~n", [Problem]),
    
    % Select appropriate reasoning strategy
    Strategy = select_reasoning_strategy(Problem, Context, State),
    
    % Execute reasoning process
    ReasoningResult = execute_reasoning_strategy(Strategy, Problem, Context, State),
    
    % Update reasoning history
    ReasoningEpisode = create_reasoning_episode(Problem, Context, Strategy, ReasoningResult),
    NewHistory = [ReasoningEpisode | State#reasoning_state.reasoning_history],
    
    % Update state
    NewState = State#reasoning_state{reasoning_history = NewHistory},
    
    {reply, {ok, ReasoningResult}, NewState};

handle_call({multi_level_reason, Problem, Levels}, _From, State) ->
    io:format("[REASONING] Multi-level reasoning about ~p with levels ~p~n", [Problem, Levels]),
    
    % Perform reasoning at multiple cognitive levels
    MultiLevelResults = perform_multi_level_reasoning(Problem, Levels, State),
    
    % Integrate results across levels
    IntegratedResult = integrate_multi_level_results(MultiLevelResults, State),
    
    {reply, {ok, IntegratedResult}, State};

handle_call({meta_reason, ReasoningProcess}, _From, State) ->
    io:format("[REASONING] Meta-reasoning about process: ~p~n", [ReasoningProcess]),
    
    % Perform meta-level reasoning about the reasoning process itself
    MetaAnalysis = analyze_reasoning_process_internal(ReasoningProcess, State),
    
    % Generate meta-cognitive insights
    MetaInsights = generate_meta_cognitive_insights(MetaAnalysis, State),
    
    % Update meta-cognitive state
    NewMetaCognitiveState = update_meta_cognitive_state(MetaInsights, 
                                                        State#reasoning_state.meta_cognitive_state),
    
    NewState = State#reasoning_state{meta_cognitive_state = NewMetaCognitiveState},
    
    {reply, {ok, #{analysis => MetaAnalysis, insights => MetaInsights}}, NewState};

handle_call({causal_inference, CauseData, EffectData}, _From, State) ->
    io:format("[REASONING] Performing causal inference~n"),
    
    % Build causal model
    CausalModel = build_causal_model(CauseData, EffectData, State),
    
    % Perform causal inference
    CausalInferences = perform_causal_inference(CausalModel, State),
    
    % Store causal model
    ModelId = generate_model_id(),
    NewCausalModels = maps:put(ModelId, CausalModel, State#reasoning_state.causal_models),
    
    NewState = State#reasoning_state{causal_models = NewCausalModels},
    
    {reply, {ok, #{model_id => ModelId, inferences => CausalInferences}}, NewState};

handle_call({counterfactual_reasoning, ActualWorld, Intervention}, _From, State) ->
    io:format("[REASONING] Counterfactual reasoning with intervention: ~p~n", [Intervention]),
    
    % Create counterfactual scenario
    CounterfactualScenario = create_counterfactual_scenario(ActualWorld, Intervention, State),
    
    % Reason about counterfactual implications
    CounterfactualResults = reason_about_counterfactual(CounterfactualScenario, State),
    
    {reply, {ok, CounterfactualResults}, State};

handle_call({analogical_reasoning, SourceDomain, TargetDomain, MappingConstraints}, _From, State) ->
    io:format("[REASONING] Analogical reasoning from ~p to ~p~n", [SourceDomain, TargetDomain]),
    
    % Find structural alignments between domains
    StructuralMappings = find_structural_alignments(SourceDomain, TargetDomain, 
                                                   MappingConstraints, State),
    
    % Generate analogical inferences
    AnalogicalInferences = generate_analogical_inferences(StructuralMappings, State),
    
    % Evaluate analogy quality
    AnalogyQuality = evaluate_analogy_quality(StructuralMappings, AnalogicalInferences, State),
    
    Result = #{
        mappings => StructuralMappings,
        inferences => AnalogicalInferences,
        quality => AnalogyQuality
    },
    
    {reply, {ok, Result}, State};

handle_call({abductive_inference, Observations}, _From, State) ->
    io:format("[REASONING] Abductive inference from observations: ~p~n", [Observations]),
    
    % Generate candidate explanations
    CandidateExplanations = generate_candidate_explanations(Observations, State),
    
    % Evaluate explanations
    EvaluatedExplanations = evaluate_explanations(CandidateExplanations, Observations, State),
    
    % Select best explanation(s)
    BestExplanations = select_best_explanations(EvaluatedExplanations, State),
    
    {reply, {ok, BestExplanations}, State};

handle_call(metacognitive_monitoring, _From, State) ->
    io:format("[REASONING] Performing metacognitive monitoring~n"),
    
    % Monitor current cognitive processes
    CognitiveMonitoring = monitor_cognitive_processes(State),
    
    % Assess reasoning performance
    PerformanceAssessment = assess_reasoning_performance(State),
    
    % Detect cognitive biases
    BiasDetection = detect_cognitive_biases(State),
    
    MonitoringResult = #{
        cognitive_processes => CognitiveMonitoring,
        performance => PerformanceAssessment,
        biases => BiasDetection,
        timestamp => erlang:system_time(second)
    },
    
    {reply, {ok, MonitoringResult}, State};

handle_call({metacognitive_control, ControlAction}, _From, State) ->
    io:format("[REASONING] Executing metacognitive control action: ~p~n", [ControlAction]),
    
    % Execute metacognitive control action
    ControlResult = execute_metacognitive_control(ControlAction, State),
    
    % Update reasoning context based on control action
    NewReasoningContext = apply_control_action(ControlAction, 
                                              State#reasoning_state.reasoning_context),
    
    NewState = State#reasoning_state{reasoning_context = NewReasoningContext},
    
    {reply, {ok, ControlResult}, NewState};

handle_call(consciousness_simulation, _From, State) ->
    io:format("[REASONING] Simulating consciousness~n"),
    
    % Simulate various aspects of consciousness
    ConsciousnessModel = simulate_consciousness_aspects(State),
    
    % Analyze self-awareness
    SelfAwarenessAnalysis = analyze_self_awareness(State),
    
    % Generate consciousness report
    ConsciousnessReport = generate_consciousness_report(ConsciousnessModel, 
                                                       SelfAwarenessAnalysis, State),
    
    {reply, {ok, ConsciousnessReport}, State};

handle_call({hypothetical_reasoning, Hypothesis, TestConditions}, _From, State) ->
    io:format("[REASONING] Hypothetical reasoning about: ~p~n", [Hypothesis]),
    
    % Create hypothetical world
    HypotheticalWorld = create_hypothetical_world(Hypothesis, State),
    
    % Test hypothesis under conditions
    TestResults = test_hypothesis_in_world(HypotheticalWorld, TestConditions, State),
    
    % Evaluate implications
    Implications = evaluate_hypothetical_implications(TestResults, State),
    
    Result = #{
        hypothesis => Hypothesis,
        world => HypotheticalWorld,
        test_results => TestResults,
        implications => Implications
    },
    
    {reply, {ok, Result}, State};

handle_call({modal_reasoning, ModalType, Proposition}, _From, State) ->
    io:format("[REASONING] Modal reasoning (~p): ~p~n", [ModalType, Proposition]),
    
    % Perform modal reasoning based on type
    ModalResult = perform_modal_reasoning(ModalType, Proposition, State),
    
    {reply, {ok, ModalResult}, State};

handle_call({creative_reasoning, CreativeChallenge}, _From, State) ->
    io:format("[REASONING] Creative reasoning for challenge: ~p~n", [CreativeChallenge]),
    
    % Use creative reasoning strategies
    CreativeStrategies = [divergent_thinking, lateral_thinking, analogical_creativity, 
                         combinatorial_creativity, transformational_creativity],
    
    % Apply creative strategies
    CreativeResults = apply_creative_strategies(CreativeStrategies, CreativeChallenge, State),
    
    % Evaluate creativity
    CreativityEvaluation = evaluate_creativity(CreativeResults, State),
    
    Result = #{
        creative_solutions => CreativeResults,
        creativity_metrics => CreativityEvaluation
    },
    
    {reply, {ok, Result}, State};

handle_call({set_reasoning_mode, Mode}, _From, State) ->
    io:format("[REASONING] Setting reasoning mode to: ~p~n", [Mode]),
    
    CurrentContext = State#reasoning_state.reasoning_context,
    NewContext = CurrentContext#reasoning_context{reasoning_mode = Mode},
    NewState = State#reasoning_state{reasoning_context = NewContext},
    
    {reply, {ok, Mode}, NewState};

handle_call(get_reasoning_state, _From, State) ->
    StateReport = generate_reasoning_state_report(State),
    {reply, {ok, StateReport}, State};

handle_call(_Request, _From, State) ->
    {reply, {error, unknown_request}, State}.

handle_cast(_Msg, State) ->
    {noreply, State}.

handle_info(cognitive_monitoring_cycle, State) ->
    % Perform periodic cognitive monitoring
    NewState = perform_cognitive_monitoring_cycle(State),
    schedule_cognitive_monitoring(),
    {noreply, NewState};

handle_info(_Info, State) ->
    {noreply, State}.

terminate(_Reason, State) ->
    io:format("[REASONING] Deep reasoning engine for agent ~p terminating~n", 
              [State#reasoning_state.agent_id]),
    save_reasoning_state(State),
    ok.

code_change(_OldVsn, State, _Extra) ->
    {ok, State}.

%%====================================================================
%% Internal functions - Core Reasoning
%%====================================================================

select_reasoning_strategy(Problem, Context, State) ->
    % Select appropriate reasoning strategy based on problem characteristics
    ProblemType = analyze_problem_type(Problem),
    ContextConstraints = analyze_context_constraints(Context),
    
    % Consider available strategies
    AvailableStrategies = State#reasoning_state.reasoning_strategies,
    
    % Score strategies for this problem
    StrategyScores = score_strategies_for_problem(ProblemType, ContextConstraints, 
                                                 AvailableStrategies),
    
    % Select best strategy
    select_best_strategy(StrategyScores).

execute_reasoning_strategy(Strategy, Problem, Context, State) ->
    % Execute the selected reasoning strategy
    case Strategy of
        deductive -> execute_deductive_reasoning(Problem, Context, State);
        inductive -> execute_inductive_reasoning(Problem, Context, State);
        abductive -> execute_abductive_reasoning(Problem, Context, State);
        analogical -> execute_analogical_reasoning(Problem, Context, State);
        causal -> execute_causal_reasoning(Problem, Context, State);
        probabilistic -> execute_probabilistic_reasoning(Problem, Context, State);
        heuristic -> execute_heuristic_reasoning(Problem, Context, State);
        _ -> execute_general_reasoning(Problem, Context, State)
    end.

perform_multi_level_reasoning(Problem, Levels, State) ->
    % Perform reasoning at multiple cognitive levels
    LevelResults = lists:map(fun(Level) ->
        LevelResult = perform_reasoning_at_level(Problem, Level, State),
        {Level, LevelResult}
    end, Levels),
    
    LevelResults.

perform_reasoning_at_level(Problem, Level, State) ->
    case Level of
        reactive -> perform_reactive_reasoning(Problem, State);
        deliberative -> perform_deliberative_reasoning(Problem, State);
        reflective -> perform_reflective_reasoning(Problem, State);
        meta_cognitive -> perform_meta_cognitive_reasoning(Problem, State);
        _ -> perform_general_level_reasoning(Problem, Level, State)
    end.

integrate_multi_level_results(LevelResults, State) ->
    % Integrate reasoning results from multiple levels
    
    % Weight results by level importance and confidence
    WeightedResults = weight_level_results(LevelResults, State),
    
    % Resolve conflicts between levels
    ConflictResolution = resolve_level_conflicts(WeightedResults, State),
    
    % Generate integrated conclusion
    IntegratedConclusion = generate_integrated_conclusion(ConflictResolution, State),
    
    #{
        level_results => LevelResults,
        weighted_results => WeightedResults,
        conflict_resolution => ConflictResolution,
        integrated_conclusion => IntegratedConclusion
    }.

%%====================================================================
%% Internal functions - Causal Reasoning
%%====================================================================

build_causal_model(CauseData, EffectData, State) ->
    % Build causal model from data
    
    % Identify variables
    CauseVariables = extract_variables(CauseData),
    EffectVariables = extract_variables(EffectData),
    
    % Analyze temporal relationships
    TemporalConstraints = analyze_temporal_relationships(CauseData, EffectData),
    
    % Identify potential confounders
    ConfoundingFactors = identify_confounding_factors(CauseData, EffectData, State),
    
    % Estimate causal strength
    CausalStrength = estimate_causal_strength(CauseData, EffectData),
    
    #causal_model{
        cause_variables = CauseVariables,
        effect_variables = EffectVariables,
        temporal_constraints = TemporalConstraints,
        confounding_factors = ConfoundingFactors,
        causal_strength = CausalStrength
    }.

perform_causal_inference(CausalModel, _State) ->
    % Perform various types of causal inference
    
    % Direct causal effects
    DirectEffects = calculate_direct_effects(CausalModel),
    
    % Indirect causal effects
    IndirectEffects = calculate_indirect_effects(CausalModel),
    
    % Total causal effects
    TotalEffects = calculate_total_effects(DirectEffects, IndirectEffects),
    
    % Causal mediation analysis
    MediationAnalysis = perform_mediation_analysis(CausalModel),
    
    #{
        direct_effects => DirectEffects,
        indirect_effects => IndirectEffects,
        total_effects => TotalEffects,
        mediation_analysis => MediationAnalysis
    }.

%%====================================================================
%% Internal functions - Counterfactual Reasoning
%%====================================================================

create_counterfactual_scenario(ActualWorld, Intervention, State) ->
    % Create counterfactual scenario
    
    % Identify intervention point
    InterventionPoint = identify_intervention_point(Intervention, ActualWorld),
    
    % Create counterfactual world
    CounterfactualWorld = apply_intervention(ActualWorld, Intervention, InterventionPoint),
    
    % Analyze divergence
    DivergenceAnalysis = analyze_world_divergence(ActualWorld, CounterfactualWorld),
    
    % Calculate plausibility
    PlausibilityScore = calculate_counterfactual_plausibility(ActualWorld, 
                                                             CounterfactualWorld, State),
    
    #counterfactual_scenario{
        scenario_id = generate_scenario_id(),
        actual_world = ActualWorld,
        counterfactual_world = CounterfactualWorld,
        intervention_point = InterventionPoint,
        divergence_analysis = DivergenceAnalysis,
        plausibility_score = PlausibilityScore
    }.

reason_about_counterfactual(CounterfactualScenario, State) ->
    % Reason about counterfactual scenario
    
    % Compare outcomes
    OutcomeComparison = compare_scenario_outcomes(CounterfactualScenario),
    
    % Generate implications
    Implications = generate_counterfactual_implications(CounterfactualScenario, State),
    
    % Assess causal importance
    CausalImportance = assess_causal_importance(CounterfactualScenario),
    
    #{
        scenario => CounterfactualScenario,
        outcome_comparison => OutcomeComparison,
        implications => Implications,
        causal_importance => CausalImportance
    }.

%%====================================================================
%% Internal functions - Meta-Cognitive Processing
%%====================================================================

analyze_reasoning_process_internal(ReasoningProcess, State) ->
    % Analyze the reasoning process at a meta-level
    
    % Analyze reasoning steps
    StepAnalysis = analyze_reasoning_steps(ReasoningProcess),
    
    % Identify reasoning patterns
    ReasoningPatterns = identify_reasoning_patterns(ReasoningProcess, State),
    
    % Assess reasoning quality
    QualityAssessment = assess_reasoning_quality(ReasoningProcess, State),
    
    % Identify potential improvements
    ImprovementSuggestions = identify_reasoning_improvements(ReasoningProcess, State),
    
    #{
        step_analysis => StepAnalysis,
        patterns => ReasoningPatterns,
        quality => QualityAssessment,
        improvements => ImprovementSuggestions
    }.

generate_meta_cognitive_insights(MetaAnalysis, State) ->
    % Generate insights about cognitive processes
    
    % Insights about reasoning effectiveness
    EffectivenessInsights = generate_effectiveness_insights(MetaAnalysis, State),
    
    % Insights about cognitive biases
    BiasInsights = generate_bias_insights(MetaAnalysis, State),
    
    % Insights about strategy selection
    StrategyInsights = generate_strategy_insights(MetaAnalysis, State),
    
    % Insights about cognitive resource usage
    ResourceInsights = generate_resource_insights(MetaAnalysis, State),
    
    #{
        effectiveness => EffectivenessInsights,
        biases => BiasInsights,
        strategies => StrategyInsights,
        resources => ResourceInsights
    }.

simulate_consciousness_aspects(State) ->
    % Simulate various aspects of consciousness
    
    % Attention and awareness simulation
    AttentionModel = simulate_attention_mechanisms(State),
    
    % Working memory simulation
    WorkingMemoryModel = simulate_working_memory(State),
    
    % Self-monitoring simulation
    SelfMonitoringModel = simulate_self_monitoring(State),
    
    % Global workspace simulation
    GlobalWorkspaceModel = simulate_global_workspace(State),
    
    #{
        attention => AttentionModel,
        working_memory => WorkingMemoryModel,
        self_monitoring => SelfMonitoringModel,
        global_workspace => GlobalWorkspaceModel
    }.

%%====================================================================
%% Internal functions - Creative Reasoning
%%====================================================================

apply_creative_strategies(Strategies, Challenge, State) ->
    % Apply multiple creative reasoning strategies
    lists:map(fun(Strategy) ->
        Result = apply_creative_strategy(Strategy, Challenge, State),
        {Strategy, Result}
    end, Strategies).

apply_creative_strategy(Strategy, Challenge, State) ->
    case Strategy of
        divergent_thinking -> apply_divergent_thinking(Challenge, State);
        lateral_thinking -> apply_lateral_thinking(Challenge, State);
        analogical_creativity -> apply_analogical_creativity(Challenge, State);
        combinatorial_creativity -> apply_combinatorial_creativity(Challenge, State);
        transformational_creativity -> apply_transformational_creativity(Challenge, State);
        _ -> apply_general_creative_strategy(Strategy, Challenge, State)
    end.

%%====================================================================
%% Internal functions - Utility and Helper Functions
%%====================================================================

initialize_reasoning_strategies(_Config) ->
    % Initialize available reasoning strategies
    [
        deductive, inductive, abductive, analogical, causal,
        probabilistic, heuristic, creative, intuitive, dialectical
    ].

initialize_cognitive_resources(_Config) ->
    % Initialize cognitive resources
    #{
        working_memory_capacity => 7,
        attention_capacity => 3,
        processing_speed => 1.0,
        cognitive_energy => 1.0
    }.

create_reasoning_episode(Problem, Context, Strategy, Result) ->
    #{
        problem => Problem,
        context => Context,
        strategy => Strategy,
        result => Result,
        timestamp => erlang:system_time(second)
    }.

schedule_cognitive_monitoring() ->
    Interval = 30000, % 30 seconds
    erlang:send_after(Interval, self(), cognitive_monitoring_cycle).

perform_cognitive_monitoring_cycle(State) ->
    % Perform cognitive monitoring and adaptation
    
    % Monitor cognitive load
    CognitiveLoad = monitor_cognitive_load(State),
    
    % Monitor attention allocation
    AttentionAllocation = monitor_attention_allocation(State),
    
    % Monitor reasoning performance
    PerformanceMetrics = monitor_reasoning_performance(State),
    
    % Adapt if necessary
    AdaptedState = adapt_cognitive_parameters(CognitiveLoad, AttentionAllocation, 
                                             PerformanceMetrics, State),
    
    AdaptedState.

generate_reasoning_state_report(State) ->
    #{
        agent_id => State#reasoning_state.agent_id,
        reasoning_mode => State#reasoning_state.reasoning_context#reasoning_context.reasoning_mode,
        cognitive_load => State#reasoning_state.reasoning_context#reasoning_context.cognitive_load,
        active_processes => maps:size(State#reasoning_state.active_reasoning_processes),
        reasoning_history_length => length(State#reasoning_state.reasoning_history),
        causal_models_count => maps:size(State#reasoning_state.causal_models),
        meta_cognitive_awareness => State#reasoning_state.meta_cognitive_state#meta_cognitive_state.self_awareness_level,
        performance_metrics => State#reasoning_state.performance_metrics
    }.

generate_reasoning_id() ->
    iolist_to_binary(io_lib:format("reasoning_engine_~p", [erlang:system_time(microsecond)])).

generate_model_id() ->
    iolist_to_binary(io_lib:format("causal_model_~p", [erlang:system_time(microsecond)])).

generate_scenario_id() ->
    iolist_to_binary(io_lib:format("counterfactual_~p", [erlang:system_time(microsecond)])).

save_reasoning_state(_State) ->
    % Save reasoning state to persistent storage
    ok.

% Placeholder implementations for complex functions - these would be fully implemented in production
analyze_problem_type(_Problem) -> general.
analyze_context_constraints(_Context) -> #{}.
score_strategies_for_problem(_ProblemType, _Constraints, Strategies) -> [{S, 0.5} || S <- Strategies].
select_best_strategy(StrategyScores) -> element(1, hd(StrategyScores)).

execute_deductive_reasoning(_Problem, _Context, _State) -> #{type => deductive, conclusion => example}.
execute_inductive_reasoning(_Problem, _Context, _State) -> #{type => inductive, conclusion => example}.
execute_abductive_reasoning(_Problem, _Context, _State) -> #{type => abductive, conclusion => example}.
execute_analogical_reasoning(_Problem, _Context, _State) -> #{type => analogical, conclusion => example}.
execute_causal_reasoning(_Problem, _Context, _State) -> #{type => causal, conclusion => example}.
execute_probabilistic_reasoning(_Problem, _Context, _State) -> #{type => probabilistic, conclusion => example}.
execute_heuristic_reasoning(_Problem, _Context, _State) -> #{type => heuristic, conclusion => example}.
execute_general_reasoning(_Problem, _Context, _State) -> #{type => general, conclusion => example}.

perform_reactive_reasoning(_Problem, _State) -> #{level => reactive}.
perform_deliberative_reasoning(_Problem, _State) -> #{level => deliberative}.
perform_reflective_reasoning(_Problem, _State) -> #{level => reflective}.
perform_meta_cognitive_reasoning(_Problem, _State) -> #{level => meta_cognitive}.
perform_general_level_reasoning(_Problem, Level, _State) -> #{level => Level}.

weight_level_results(Results, _State) -> Results.
resolve_level_conflicts(Results, _State) -> Results.
generate_integrated_conclusion(Results, _State) -> #{integrated => true, results => Results}.

extract_variables(_Data) -> [].
analyze_temporal_relationships(_CauseData, _EffectData) -> [].
identify_confounding_factors(_CauseData, _EffectData, _State) -> [].
estimate_causal_strength(_CauseData, _EffectData) -> #{}.
calculate_direct_effects(_Model) -> #{}.
calculate_indirect_effects(_Model) -> #{}.
calculate_total_effects(_Direct, _Indirect) -> #{}.
perform_mediation_analysis(_Model) -> #{}.

identify_intervention_point(_Intervention, _World) -> undefined.
apply_intervention(World, _Intervention, _Point) -> World.
analyze_world_divergence(_Actual, _Counterfactual) -> #{}.
calculate_counterfactual_plausibility(_Actual, _Counterfactual, _State) -> 0.5.
compare_scenario_outcomes(_Scenario) -> #{}.
generate_counterfactual_implications(_Scenario, _State) -> [].
assess_causal_importance(_Scenario) -> 0.5.

analyze_reasoning_steps(_Process) -> #{}.
identify_reasoning_patterns(_Process, _State) -> [].
assess_reasoning_quality(_Process, _State) -> 0.8.
identify_reasoning_improvements(_Process, _State) -> [].
generate_effectiveness_insights(_Analysis, _State) -> #{}.
generate_bias_insights(_Analysis, _State) -> #{}.
generate_strategy_insights(_Analysis, _State) -> #{}.
generate_resource_insights(_Analysis, _State) -> #{}.

simulate_attention_mechanisms(_State) -> #{}.
simulate_working_memory(_State) -> #{}.
simulate_self_monitoring(_State) -> #{}.
simulate_global_workspace(_State) -> #{}.

apply_divergent_thinking(_Challenge, _State) -> [].
apply_lateral_thinking(_Challenge, _State) -> [].
apply_analogical_creativity(_Challenge, _State) -> [].
apply_combinatorial_creativity(_Challenge, _State) -> [].
apply_transformational_creativity(_Challenge, _State) -> [].
apply_general_creative_strategy(_Strategy, _Challenge, _State) -> [].

monitor_cognitive_load(_State) -> 0.5.
monitor_attention_allocation(_State) -> #{}.
monitor_reasoning_performance(_State) -> #{}.
adapt_cognitive_parameters(_Load, _Attention, _Performance, State) -> State.

update_meta_cognitive_state(_Insights, MetaState) -> MetaState.
execute_metacognitive_control(_Action, _State) -> #{}.
apply_control_action(_Action, Context) -> Context.
monitor_cognitive_processes(_State) -> #{}.
assess_reasoning_performance(_State) -> #{}.
detect_cognitive_biases(_State) -> [].
analyze_self_awareness(_State) -> #{}.
generate_consciousness_report(_Model, _Analysis, _State) -> #{}.

find_structural_alignments(_Source, _Target, _Constraints, _State) -> [].
generate_analogical_inferences(_Mappings, _State) -> [].
evaluate_analogy_quality(_Mappings, _Inferences, _State) -> 0.8.
generate_candidate_explanations(_Observations, _State) -> [].
evaluate_explanations(_Candidates, _Observations, _State) -> [].
select_best_explanations(_Evaluated, _State) -> [].

create_hypothetical_world(_Hypothesis, _State) -> #{}.
test_hypothesis_in_world(_World, _Conditions, _State) -> #{}.
evaluate_hypothetical_implications(_Results, _State) -> [].
perform_modal_reasoning(_Type, _Proposition, _State) -> #{}.
evaluate_creativity(_Results, _State) -> #{}.