-module(self_reflection_system). -behaviour(gen_statem). -export([start_link/0, start_link/1]). -export([initiate_self_reflection/1, assess_performance/1, evaluate_decisions/1, analyze_learning_progress/1, reflect_on_goals/1, examine_biases/1, update_self_model/2, get_self_assessment/1, trigger_metacognition/1]). -export([init/1, callback_mode/0, terminate/3, code_change/4]). -export([idle/3, self_assessment/3, metacognitive_analysis/3, bias_examination/3, decision_evaluation/3, learning_reflection/3, goal_alignment/3, self_model_update/3, insight_integration/3, wisdom_synthesis/3]). -record(cognitive_profile, { strengths = [] :: [atom()], weaknesses = [] :: [atom()], learning_style = undefined :: undefined | atom(), decision_patterns = #{} :: #{atom() => term()}, bias_tendencies = [] :: [atom()], metacognitive_awareness = 0.0 :: float(), self_efficacy = 0.0 :: float(), adaptability_score = 0.0 :: float(), emotional_intelligence = 0.0 :: float(), critical_thinking = 0.0 :: float() }). -record(reflection_insight, { type :: atom(), content :: term(), confidence = 0.0 :: float(), impact_level = low :: low | medium | high | critical, actionable_items = [] :: [term()], timestamp :: erlang:timestamp(), validation_status = pending :: pending | validated | rejected }). -record(self_reflection_data, { session_id :: term(), cognitive_profile = #cognitive_profile{} :: #cognitive_profile{}, historical_profiles = [] :: [#cognitive_profile{}], current_insights = [] :: [#reflection_insight{}], decision_history = [] :: [term()], learning_episodes = [] :: [term()], goal_evolution = [] :: [term()], performance_metrics = #{} :: #{atom() => number()}, metacognitive_state = #{ awareness_level => 0.0, confidence_level => 0.0, reflection_depth => shallow, cognitive_load => low } :: #{atom() => term()}, self_model = #{ identity => undefined, capabilities => [], limitations => [], values => [], beliefs => [], assumptions => [] } :: #{atom() => term()}, reflection_triggers = #{ performance_threshold => 0.7, decision_complexity => medium, learning_plateau => true, goal_misalignment => true, bias_detection => true } :: #{atom() => term()}, wisdom_accumulation = #{ lessons_learned => [], principles_discovered => [], mental_models => [], heuristics => [], patterns => [] } :: #{atom() => [term()]}, self_improvement_plan = [] :: [term()], reflection_statistics = #{} :: #{atom() => term()}, observers = [] :: [pid()], continuous_mode = false :: boolean(), reflection_interval = 10000 :: pos_integer(), start_time :: erlang:timestamp() }). start_link() -> gen_statem:start_link(?MODULE, [], []). start_link(Options) -> gen_statem:start_link(?MODULE, Options, []). initiate_self_reflection(Pid) -> gen_statem:call(Pid, initiate_self_reflection). assess_performance(Pid) -> gen_statem:call(Pid, assess_performance). evaluate_decisions(Pid) -> gen_statem:call(Pid, evaluate_decisions). analyze_learning_progress(Pid) -> gen_statem:call(Pid, analyze_learning_progress). reflect_on_goals(Pid) -> gen_statem:call(Pid, reflect_on_goals). examine_biases(Pid) -> gen_statem:call(Pid, examine_biases). update_self_model(Pid, Updates) -> gen_statem:call(Pid, {update_self_model, Updates}). get_self_assessment(Pid) -> gen_statem:call(Pid, get_self_assessment). trigger_metacognition(Pid) -> gen_statem:call(Pid, trigger_metacognition). init(Options) -> Data = #self_reflection_data{ session_id = make_ref(), continuous_mode = proplists:get_value(continuous, Options, false), reflection_interval = proplists:get_value(interval, Options, 10000), start_time = erlang:timestamp(), cognitive_profile = initialize_cognitive_profile(), reflection_triggers = initialize_reflection_triggers(Options), reflection_statistics = #{ reflection_sessions => 0, insights_generated => 0, biases_detected => 0, decisions_evaluated => 0, learning_episodes_analyzed => 0, self_model_updates => 0, metacognitive_events => 0 } }, {ok, idle, Data}. callback_mode() -> [state_functions, state_enter]. idle(enter, _OldState, Data) -> case Data#self_reflection_data.continuous_mode of true -> {keep_state, Data, [{state_timeout, Data#self_reflection_data.reflection_interval, continuous_reflection}]}; false -> {keep_state, Data} end; idle({call, From}, initiate_self_reflection, Data) -> ReflectionData = begin_reflection_session(Data), {next_state, self_assessment, ReflectionData, [{reply, From, ok}]}; idle({call, From}, assess_performance, Data) -> {next_state, self_assessment, Data, [{reply, From, ok}, {state_timeout, 0, performance_focus}]}; idle({call, From}, examine_biases, Data) -> {next_state, bias_examination, Data, [{reply, From, ok}]}; idle({call, From}, trigger_metacognition, Data) -> {next_state, metacognitive_analysis, Data, [{reply, From, ok}]}; idle(state_timeout, continuous_reflection, Data) -> {next_state, self_assessment, begin_reflection_session(Data)}; idle(EventType, Event, Data) -> handle_common_events(EventType, Event, Data). self_assessment(enter, _OldState, Data) -> AssessmentData = conduct_comprehensive_self_assessment(Data), UpdatedStats = increment_stat(reflection_sessions, Data#self_reflection_data.reflection_statistics), {next_state, metacognitive_analysis, AssessmentData#self_reflection_data{reflection_statistics = UpdatedStats}}; self_assessment(state_timeout, performance_focus, Data) -> PerformanceData = focus_on_performance_assessment(Data), {next_state, decision_evaluation, PerformanceData}; self_assessment(EventType, Event, Data) -> handle_common_events(EventType, Event, Data). metacognitive_analysis(enter, _OldState, Data) -> MetacognitiveData = perform_metacognitive_analysis(Data), UpdatedStats = increment_stat(metacognitive_events, Data#self_reflection_data.reflection_statistics), {next_state, bias_examination, MetacognitiveData#self_reflection_data{reflection_statistics = UpdatedStats}}; metacognitive_analysis(EventType, Event, Data) -> handle_common_events(EventType, Event, Data). bias_examination(enter, _OldState, Data) -> BiasData = examine_cognitive_biases(Data), case detect_significant_biases(BiasData) of {biases_found, BiasInsights} -> InsightData = record_bias_insights(BiasInsights, BiasData), UpdatedStats = increment_stat(biases_detected, InsightData#self_reflection_data.reflection_statistics), {next_state, decision_evaluation, InsightData#self_reflection_data{reflection_statistics = UpdatedStats}}; no_significant_biases -> {next_state, decision_evaluation, BiasData} end; bias_examination(EventType, Event, Data) -> handle_common_events(EventType, Event, Data). decision_evaluation(enter, _OldState, Data) -> DecisionData = evaluate_recent_decisions(Data), case analyze_decision_quality(DecisionData) of {insights_available, DecisionInsights} -> InsightData = integrate_decision_insights(DecisionInsights, DecisionData), UpdatedStats = increment_stat(decisions_evaluated, InsightData#self_reflection_data.reflection_statistics), {next_state, learning_reflection, InsightData#self_reflection_data{reflection_statistics = UpdatedStats}}; no_significant_insights -> {next_state, learning_reflection, DecisionData} end; decision_evaluation(EventType, Event, Data) -> handle_common_events(EventType, Event, Data). learning_reflection(enter, _OldState, Data) -> LearningData = analyze_learning_progress_and_patterns(Data), case identify_learning_insights(LearningData) of {learning_insights, Insights} -> InsightData = integrate_learning_insights(Insights, LearningData), UpdatedStats = increment_stat(learning_episodes_analyzed, InsightData#self_reflection_data.reflection_statistics), {next_state, goal_alignment, InsightData#self_reflection_data{reflection_statistics = UpdatedStats}}; no_learning_insights -> {next_state, goal_alignment, LearningData} end; learning_reflection(EventType, Event, Data) -> handle_common_events(EventType, Event, Data). goal_alignment(enter, _OldState, Data) -> GoalData = assess_goal_alignment_and_evolution(Data), case evaluate_goal_coherence(GoalData) of {alignment_issues, Issues} -> UpdatedData = address_goal_misalignment(Issues, GoalData), {next_state, self_model_update, UpdatedData}; goals_aligned -> {next_state, self_model_update, GoalData} end; goal_alignment(EventType, Event, Data) -> handle_common_events(EventType, Event, Data). self_model_update(enter, _OldState, Data) -> ModelData = update_self_model_based_on_insights(Data), case significant_model_changes(ModelData) of true -> UpdatedStats = increment_stat(self_model_updates, ModelData#self_reflection_data.reflection_statistics), {next_state, insight_integration, ModelData#self_reflection_data{reflection_statistics = UpdatedStats}}; false -> {next_state, insight_integration, ModelData} end; self_model_update(EventType, Event, Data) -> handle_common_events(EventType, Event, Data). insight_integration(enter, _OldState, Data) -> IntegrationData = integrate_all_insights(Data), PlanData = formulate_self_improvement_plan(IntegrationData), UpdatedStats = increment_stat(insights_generated, PlanData#self_reflection_data.reflection_statistics), {next_state, wisdom_synthesis, PlanData#self_reflection_data{reflection_statistics = UpdatedStats}}; insight_integration(EventType, Event, Data) -> handle_common_events(EventType, Event, Data). wisdom_synthesis(enter, _OldState, Data) -> WisdomData = synthesize_wisdom_from_reflection(Data), FinalData = complete_reflection_cycle(WisdomData), case Data#self_reflection_data.continuous_mode of true -> {next_state, idle, FinalData}; false -> {keep_state, FinalData} end; wisdom_synthesis({call, From}, get_self_assessment, Data) -> Assessment = compile_self_assessment_report(Data), {keep_state, Data, [{reply, From, {ok, Assessment}}]}; wisdom_synthesis(EventType, Event, Data) -> handle_common_events(EventType, Event, Data). terminate(_Reason, _State, _Data) -> ok. code_change(_Vsn, State, Data, _Extra) -> {ok, State, Data}. begin_reflection_session(Data) -> Data#self_reflection_data{ session_id = make_ref(), current_insights = [], start_time = erlang:timestamp() }. conduct_comprehensive_self_assessment(Data) -> Profile = Data#self_reflection_data.cognitive_profile, StrengthsAssessment = assess_cognitive_strengths(Profile, Data), WeaknessesAssessment = identify_cognitive_weaknesses(Profile, Data), MetacognitiveAssessment = evaluate_metacognitive_awareness(Profile, Data), AdaptabilityAssessment = assess_adaptability(Profile, Data), UpdatedProfile = Profile#cognitive_profile{ strengths = StrengthsAssessment, weaknesses = WeaknessesAssessment, metacognitive_awareness = MetacognitiveAssessment, adaptability_score = AdaptabilityAssessment }, Data#self_reflection_data{cognitive_profile = UpdatedProfile}. focus_on_performance_assessment(Data) -> PerformanceMetrics = collect_performance_data(Data), PerformanceAnalysis = analyze_performance_trends(PerformanceMetrics), PerformanceInsights = generate_performance_insights(PerformanceAnalysis), Data#self_reflection_data{ performance_metrics = PerformanceMetrics, current_insights = PerformanceInsights ++ Data#self_reflection_data.current_insights }. perform_metacognitive_analysis(Data) -> MetacognitiveState = Data#self_reflection_data.metacognitive_state, AwarenessAnalysis = analyze_self_awareness_levels(MetacognitiveState, Data), ConfidenceAnalysis = evaluate_confidence_calibration(MetacognitiveState, Data), ReflectionDepthAnalysis = assess_reflection_depth_effectiveness(MetacognitiveState, Data), CognitiveLoadAnalysis = analyze_cognitive_load_patterns(MetacognitiveState, Data), UpdatedMetacognitiveState = #{ awareness_level => AwarenessAnalysis, confidence_level => ConfidenceAnalysis, reflection_depth => ReflectionDepthAnalysis, cognitive_load => CognitiveLoadAnalysis }, Data#self_reflection_data{metacognitive_state = UpdatedMetacognitiveState}. examine_cognitive_biases(Data) -> Profile = Data#self_reflection_data.cognitive_profile, DecisionHistory = Data#self_reflection_data.decision_history, ConfirmationBias = detect_confirmation_bias(DecisionHistory), AnchoringBias = detect_anchoring_bias(DecisionHistory), AvailabilityBias = detect_availability_bias(DecisionHistory), OverconfidenceBias = detect_overconfidence_bias(Profile, DecisionHistory), DetectedBiases = [B || B <- [ConfirmationBias, AnchoringBias, AvailabilityBias, OverconfidenceBias], B =/= none], UpdatedProfile = Profile#cognitive_profile{bias_tendencies = DetectedBiases}, Data#self_reflection_data{cognitive_profile = UpdatedProfile}. detect_significant_biases(Data) -> Biases = Data#self_reflection_data.cognitive_profile#cognitive_profile.bias_tendencies, case length(Biases) of 0 -> no_significant_biases; Count when Count > 0 -> BiasInsights = [create_bias_insight(Bias) || Bias <- Biases], {biases_found, BiasInsights} end. evaluate_recent_decisions(Data) -> DecisionHistory = Data#self_reflection_data.decision_history, RecentDecisions = lists:sublist(DecisionHistory, 10), DecisionAnalysis = [analyze_single_decision(Decision, Data) || Decision <- RecentDecisions], QualityMetrics = calculate_decision_quality_metrics(DecisionAnalysis), Data#self_reflection_data{ performance_metrics = maps:merge(Data#self_reflection_data.performance_metrics, QualityMetrics) }. analyze_decision_quality(Data) -> QualityMetrics = maps:get(decision_quality, Data#self_reflection_data.performance_metrics, 0.0), case QualityMetrics > 0.7 of true -> no_significant_insights; false -> DecisionInsights = generate_decision_improvement_insights(Data), {insights_available, DecisionInsights} end. analyze_learning_progress_and_patterns(Data) -> LearningEpisodes = Data#self_reflection_data.learning_episodes, LearningVelocity = calculate_learning_velocity(LearningEpisodes), LearningEfficiency = assess_learning_efficiency(LearningEpisodes), KnowledgeRetention = evaluate_knowledge_retention(LearningEpisodes), TransferLearning = assess_transfer_learning_capability(LearningEpisodes), LearningMetrics = #{ velocity => LearningVelocity, efficiency => LearningEfficiency, retention => KnowledgeRetention, transfer => TransferLearning }, Data#self_reflection_data{ performance_metrics = maps:merge(Data#self_reflection_data.performance_metrics, LearningMetrics) }. identify_learning_insights(Data) -> LearningMetrics = maps:get(velocity, Data#self_reflection_data.performance_metrics, 0.0), case LearningMetrics < 0.6 of true -> LearningInsights = generate_learning_improvement_insights(Data), {learning_insights, LearningInsights}; false -> no_learning_insights end. assess_goal_alignment_and_evolution(Data) -> GoalEvolution = Data#self_reflection_data.goal_evolution, SelfModel = Data#self_reflection_data.self_model, GoalCoherence = assess_internal_goal_coherence(GoalEvolution), ValueAlignment = evaluate_goal_value_alignment(GoalEvolution, SelfModel), CapabilityAlignment = assess_goal_capability_alignment(GoalEvolution, SelfModel), Data#self_reflection_data{ performance_metrics = maps:merge(Data#self_reflection_data.performance_metrics, #{ goal_coherence => GoalCoherence, value_alignment => ValueAlignment, capability_alignment => CapabilityAlignment }) }. evaluate_goal_coherence(Data) -> Coherence = maps:get(goal_coherence, Data#self_reflection_data.performance_metrics, 1.0), case Coherence < 0.7 of true -> Issues = identify_goal_coherence_issues(Data), {alignment_issues, Issues}; false -> goals_aligned end. update_self_model_based_on_insights(Data) -> Insights = Data#self_reflection_data.current_insights, SelfModel = Data#self_reflection_data.self_model, UpdatedModel = apply_insights_to_self_model(Insights, SelfModel), Data#self_reflection_data{self_model = UpdatedModel}. significant_model_changes(Data) -> length(Data#self_reflection_data.current_insights) > 3. integrate_all_insights(Data) -> Insights = Data#self_reflection_data.current_insights, IntegratedInsights = synthesize_insights(Insights), ValidatedInsights = validate_insight_consistency(IntegratedInsights), Data#self_reflection_data{current_insights = ValidatedInsights}. formulate_self_improvement_plan(Data) -> Insights = Data#self_reflection_data.current_insights, CurrentPlan = Data#self_reflection_data.self_improvement_plan, NewActionItems = generate_action_items_from_insights(Insights), UpdatedPlan = integrate_new_action_items(NewActionItems, CurrentPlan), PrioritizedPlan = prioritize_improvement_actions(UpdatedPlan), Data#self_reflection_data{self_improvement_plan = PrioritizedPlan}. synthesize_wisdom_from_reflection(Data) -> Insights = Data#self_reflection_data.current_insights, WisdomAccumulation = Data#self_reflection_data.wisdom_accumulation, NewLessons = extract_lessons_learned(Insights), NewPrinciples = derive_principles(Insights), NewMentalModels = update_mental_models(Insights, WisdomAccumulation), NewHeuristics = develop_heuristics(Insights), UpdatedWisdom = #{ lessons_learned => NewLessons ++ maps:get(lessons_learned, WisdomAccumulation, []), principles_discovered => NewPrinciples ++ maps:get(principles_discovered, WisdomAccumulation, []), mental_models => NewMentalModels, heuristics => NewHeuristics ++ maps:get(heuristics, WisdomAccumulation, []), patterns => update_wisdom_patterns(Insights, WisdomAccumulation) }, Data#self_reflection_data{wisdom_accumulation = UpdatedWisdom}. complete_reflection_cycle(Data) -> Profile = Data#self_reflection_data.cognitive_profile, HistoricalProfiles = [Profile | Data#self_reflection_data.historical_profiles], Data#self_reflection_data{ historical_profiles = lists:sublist(HistoricalProfiles, 20), current_insights = [] }. compile_self_assessment_report(Data) -> #{ session_id => Data#self_reflection_data.session_id, timestamp => erlang:timestamp(), cognitive_profile => Data#self_reflection_data.cognitive_profile, metacognitive_state => Data#self_reflection_data.metacognitive_state, self_model => Data#self_reflection_data.self_model, current_insights => Data#self_reflection_data.current_insights, performance_metrics => Data#self_reflection_data.performance_metrics, wisdom_accumulation => Data#self_reflection_data.wisdom_accumulation, self_improvement_plan => Data#self_reflection_data.self_improvement_plan, reflection_statistics => Data#self_reflection_data.reflection_statistics }. record_bias_insights(BiasInsights, Data) -> Data#self_reflection_data{ current_insights = BiasInsights ++ Data#self_reflection_data.current_insights }. integrate_decision_insights(DecisionInsights, Data) -> Data#self_reflection_data{ current_insights = DecisionInsights ++ Data#self_reflection_data.current_insights }. integrate_learning_insights(LearningInsights, Data) -> Data#self_reflection_data{ current_insights = LearningInsights ++ Data#self_reflection_data.current_insights }. address_goal_misalignment(Issues, Data) -> MisalignmentInsights = [create_goal_misalignment_insight(Issue) || Issue <- Issues], Data#self_reflection_data{ current_insights = MisalignmentInsights ++ Data#self_reflection_data.current_insights }. increment_stat(Stat, Stats) -> maps:update_with(Stat, fun(X) -> X + 1 end, 1, Stats). handle_common_events({call, From}, get_self_assessment, Data) -> Assessment = compile_self_assessment_report(Data), {keep_state, Data, [{reply, From, {ok, Assessment}}]}; handle_common_events({call, From}, {update_self_model, Updates}, Data) -> UpdatedModel = maps:merge(Data#self_reflection_data.self_model, Updates), {keep_state, Data#self_reflection_data{self_model = UpdatedModel}, [{reply, From, ok}]}; handle_common_events({call, From}, evaluate_decisions, Data) -> {next_state, decision_evaluation, Data, [{reply, From, ok}]}; handle_common_events({call, From}, analyze_learning_progress, Data) -> {next_state, learning_reflection, Data, [{reply, From, ok}]}; handle_common_events({call, From}, reflect_on_goals, Data) -> {next_state, goal_alignment, Data, [{reply, From, ok}]}; handle_common_events(_EventType, _Event, _Data) -> {keep_state_and_data, [postpone]}. initialize_cognitive_profile() -> #cognitive_profile{ metacognitive_awareness = 0.5, self_efficacy = 0.5, adaptability_score = 0.5, emotional_intelligence = 0.5, critical_thinking = 0.5 }. initialize_reflection_triggers(Options) -> #{ performance_threshold => proplists:get_value(performance_threshold, Options, 0.7), decision_complexity => proplists:get_value(decision_complexity, Options, medium), learning_plateau => proplists:get_value(learning_plateau, Options, true), goal_misalignment => proplists:get_value(goal_misalignment, Options, true), bias_detection => proplists:get_value(bias_detection, Options, true) }. assess_cognitive_strengths(_Profile, _Data) -> []. identify_cognitive_weaknesses(_Profile, _Data) -> []. evaluate_metacognitive_awareness(_Profile, _Data) -> 0.5. assess_adaptability(_Profile, _Data) -> 0.5. collect_performance_data(_Data) -> #{}. analyze_performance_trends(_Metrics) -> #{}. generate_performance_insights(_Analysis) -> []. analyze_self_awareness_levels(_State, _Data) -> 0.5. evaluate_confidence_calibration(_State, _Data) -> 0.5. assess_reflection_depth_effectiveness(_State, _Data) -> shallow. analyze_cognitive_load_patterns(_State, _Data) -> low. detect_confirmation_bias(_History) -> none. detect_anchoring_bias(_History) -> none. detect_availability_bias(_History) -> none. detect_overconfidence_bias(_Profile, _History) -> none. create_bias_insight(Bias) -> #reflection_insight{type = bias, content = Bias}. analyze_single_decision(_Decision, _Data) -> #{}. calculate_decision_quality_metrics(_Analysis) -> #{decision_quality => 0.8}. generate_decision_improvement_insights(_Data) -> []. calculate_learning_velocity(_Episodes) -> 0.7. assess_learning_efficiency(_Episodes) -> 0.7. evaluate_knowledge_retention(_Episodes) -> 0.8. assess_transfer_learning_capability(_Episodes) -> 0.6. generate_learning_improvement_insights(_Data) -> []. assess_internal_goal_coherence(_Evolution) -> 0.8. evaluate_goal_value_alignment(_Evolution, _Model) -> 0.8. assess_goal_capability_alignment(_Evolution, _Model) -> 0.8. identify_goal_coherence_issues(_Data) -> []. apply_insights_to_self_model(_Insights, Model) -> Model. synthesize_insights(Insights) -> Insights. validate_insight_consistency(Insights) -> Insights. generate_action_items_from_insights(_Insights) -> []. integrate_new_action_items(New, Current) -> New ++ Current. prioritize_improvement_actions(Plan) -> Plan. extract_lessons_learned(_Insights) -> []. derive_principles(_Insights) -> []. update_mental_models(_Insights, Wisdom) -> maps:get(mental_models, Wisdom, []). develop_heuristics(_Insights) -> []. update_wisdom_patterns(_Insights, Wisdom) -> maps:get(patterns, Wisdom, []). create_goal_misalignment_insight(Issue) -> #reflection_insight{type = goal_misalignment, content = Issue}.