-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) -> #{}.