-module(dynamic_knowledge_graph). -behaviour(gen_server). %% Dynamic Knowledge Graph System %% Advanced knowledge representation system that dynamically constructs, %% explores, and reasons over knowledge graphs. Supports: %% - Dynamic node and edge creation/modification %% - Multi-modal knowledge representation (concepts, relations, abstractions) %% - Graph exploration algorithms and path finding %% - Knowledge inference and reasoning %% - Concept emergence and abstraction formation %% - Semantic similarity and clustering %% - Knowledge graph visualization and analysis -export([start_link/1, % Core graph operations add_node/3, add_edge/4, remove_node/2, remove_edge/3, get_node/2, get_edges_from/2, get_edges_to/2, % Knowledge operations add_concept/3, add_relation/4, add_fact/3, add_abstraction/3, query_knowledge/2, infer_knowledge/2, validate_knowledge/2, % Exploration and discovery explore_neighborhood/3, find_paths/4, discover_patterns/2, suggest_connections/2, identify_clusters/2, detect_anomalies/2, % Reasoning and inference perform_reasoning/3, generate_hypotheses/2, validate_hypothesis/3, causal_reasoning/3, analogical_reasoning/3, deductive_reasoning/3, % Graph analysis analyze_graph_structure/1, calculate_centrality/2, find_communities/2, measure_semantic_similarity/3, compute_graph_metrics/1, % Learning and adaptation learn_from_interaction/3, adapt_structure/2, evolve_concepts/2, consolidate_knowledge/1, prune_redundant_knowledge/1]). -export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]). %% Knowledge representation structures -record(knowledge_node, { id, % Unique node identifier type, % Node type (concept, entity, abstraction, etc.) properties = #{}, % Node properties and attributes content, % Main content or representation confidence = 1.0, % Confidence level (0-1) creation_time, % When node was created last_accessed, % Last access timestamp access_count = 0, % Number of times accessed source, % Source of this knowledge tags = [], % Semantic tags metadata = #{} % Additional metadata }). -record(knowledge_edge, { id, % Unique edge identifier from_node, % Source node ID to_node, % Target node ID relation_type, % Type of relationship properties = #{}, % Edge properties weight = 1.0, % Edge weight/strength confidence = 1.0, % Confidence in this relation bidirectional = false, % Whether edge is bidirectional creation_time, % When edge was created source, % Source of this relation evidence = [], % Supporting evidence metadata = #{} % Additional metadata }). -record(knowledge_pattern, { id, % Pattern identifier pattern_type, % Type of pattern (sequence, structure, etc.) nodes = [], % Nodes involved in pattern edges = [], % Edges involved in pattern frequency = 1, % How often pattern occurs confidence = 1.0, % Confidence in pattern generalization_level = 0, % Level of abstraction examples = [], % Concrete examples of pattern exceptions = [], % Known exceptions to pattern metadata = #{} % Additional pattern metadata }). -record(knowledge_abstraction, { id, % Abstraction identifier abstraction_type, % Type of abstraction concrete_instances = [], % Specific instances this abstracts abstract_properties = #{}, % Properties at abstract level abstraction_level = 1, % Level in abstraction hierarchy generalization_rules = [], % Rules for generalization specialization_rules = [], % Rules for specialization confidence = 1.0, % Confidence in abstraction metadata = #{} % Additional metadata }). -record(graph_state, { agent_id, % Associated agent nodes = #{}, % Map of node_id -> knowledge_node edges = #{}, % Map of edge_id -> knowledge_edge node_index = #{}, % Various indices for fast lookup edge_index = #{}, % Edge indices patterns = #{}, % Discovered patterns abstractions = #{}, % Formed abstractions inference_rules = [], % Rules for inference exploration_history = [], % History of explorations reasoning_cache = #{}, % Cache for reasoning results graph_metrics = #{}, % Cached graph metrics learning_parameters = #{}, % Parameters for learning evolution_history = [] % History of graph evolution }). %%==================================================================== %% API functions %%==================================================================== start_link(Config) -> AgentId = maps:get(agent_id, Config, generate_graph_id()), io:format("[KNOWLEDGE_GRAPH] Starting dynamic knowledge graph for agent ~p~n", [AgentId]), gen_server:start_link(?MODULE, [AgentId, Config], []). %% Core graph operations add_node(GraphPid, NodeId, NodeData) -> gen_server:call(GraphPid, {add_node, NodeId, NodeData}). add_edge(GraphPid, EdgeId, FromNode, ToNode) -> gen_server:call(GraphPid, {add_edge, EdgeId, FromNode, ToNode}). remove_node(GraphPid, NodeId) -> gen_server:call(GraphPid, {remove_node, NodeId}). remove_edge(GraphPid, EdgeId) -> gen_server:call(GraphPid, {remove_edge, EdgeId}). remove_edge(GraphPid, FromNodeId, ToNodeId) -> gen_server:call(GraphPid, {remove_edge_between, FromNodeId, ToNodeId}). get_node(GraphPid, NodeId) -> gen_server:call(GraphPid, {get_node, NodeId}). get_edges_from(GraphPid, NodeId) -> gen_server:call(GraphPid, {get_edges_from, NodeId}). get_edges_to(GraphPid, NodeId) -> gen_server:call(GraphPid, {get_edges_to, NodeId}). %% Knowledge operations add_concept(GraphPid, ConceptId, ConceptData) -> gen_server:call(GraphPid, {add_concept, ConceptId, ConceptData}). add_relation(GraphPid, RelationId, FromConcept, ToConcept) -> gen_server:call(GraphPid, {add_relation, RelationId, FromConcept, ToConcept}). add_fact(GraphPid, FactId, FactData) -> gen_server:call(GraphPid, {add_fact, FactId, FactData}). add_abstraction(GraphPid, AbstractionId, AbstractionData) -> gen_server:call(GraphPid, {add_abstraction, AbstractionId, AbstractionData}). query_knowledge(GraphPid, Query) -> gen_server:call(GraphPid, {query_knowledge, Query}). infer_knowledge(GraphPid, InferenceRequest) -> gen_server:call(GraphPid, {infer_knowledge, InferenceRequest}). validate_knowledge(GraphPid, KnowledgeItem) -> gen_server:call(GraphPid, {validate_knowledge, KnowledgeItem}). %% Exploration and discovery explore_neighborhood(GraphPid, StartNode, Depth) -> gen_server:call(GraphPid, {explore_neighborhood, StartNode, Depth}). find_paths(GraphPid, StartNode, EndNode, MaxDepth) -> gen_server:call(GraphPid, {find_paths, StartNode, EndNode, MaxDepth}). discover_patterns(GraphPid, PatternType) -> gen_server:call(GraphPid, {discover_patterns, PatternType}). suggest_connections(GraphPid, NodeId) -> gen_server:call(GraphPid, {suggest_connections, NodeId}). identify_clusters(GraphPid, ClusteringAlgorithm) -> gen_server:call(GraphPid, {identify_clusters, ClusteringAlgorithm}). detect_anomalies(GraphPid, AnomalyType) -> gen_server:call(GraphPid, {detect_anomalies, AnomalyType}). %% Reasoning and inference perform_reasoning(GraphPid, ReasoningType, Context) -> gen_server:call(GraphPid, {perform_reasoning, ReasoningType, Context}). generate_hypotheses(GraphPid, Domain) -> gen_server:call(GraphPid, {generate_hypotheses, Domain}). validate_hypothesis(GraphPid, Hypothesis, Evidence) -> gen_server:call(GraphPid, {validate_hypothesis, Hypothesis, Evidence}). causal_reasoning(GraphPid, Cause, Effect) -> gen_server:call(GraphPid, {causal_reasoning, Cause, Effect}). analogical_reasoning(GraphPid, SourceDomain, TargetDomain) -> gen_server:call(GraphPid, {analogical_reasoning, SourceDomain, TargetDomain}). deductive_reasoning(GraphPid, Premises) -> gen_server:call(GraphPid, {deductive_reasoning, Premises}). deductive_reasoning(GraphPid, Premises, Rules) -> gen_server:call(GraphPid, {deductive_reasoning, Premises, Rules}). %% Graph analysis analyze_graph_structure(GraphPid) -> gen_server:call(GraphPid, analyze_graph_structure). calculate_centrality(GraphPid, CentralityType) -> gen_server:call(GraphPid, {calculate_centrality, CentralityType}). find_communities(GraphPid, CommunityAlgorithm) -> gen_server:call(GraphPid, {find_communities, CommunityAlgorithm}). measure_semantic_similarity(GraphPid, Node1, Node2) -> gen_server:call(GraphPid, {measure_semantic_similarity, Node1, Node2}). compute_graph_metrics(GraphPid) -> gen_server:call(GraphPid, compute_graph_metrics). %% Learning and adaptation learn_from_interaction(GraphPid, Interaction, Outcome) -> gen_server:cast(GraphPid, {learn_from_interaction, Interaction, Outcome}). adapt_structure(GraphPid, AdaptationSignal) -> gen_server:cast(GraphPid, {adapt_structure, AdaptationSignal}). evolve_concepts(GraphPid, EvolutionPressure) -> gen_server:cast(GraphPid, {evolve_concepts, EvolutionPressure}). consolidate_knowledge(GraphPid) -> gen_server:call(GraphPid, consolidate_knowledge). prune_redundant_knowledge(GraphPid) -> gen_server:call(GraphPid, prune_redundant_knowledge). %%==================================================================== %% gen_server callbacks %%==================================================================== init([AgentId, Config]) -> process_flag(trap_exit, true), io:format("[KNOWLEDGE_GRAPH] Initializing knowledge graph for agent ~p~n", [AgentId]), % Initialize learning parameters LearningParams = #{ learning_rate => maps:get(learning_rate, Config, 0.1), forgetting_rate => maps:get(forgetting_rate, Config, 0.01), consolidation_threshold => maps:get(consolidation_threshold, Config, 10), pruning_threshold => maps:get(pruning_threshold, Config, 0.1), exploration_bias => maps:get(exploration_bias, Config, 0.2) }, State = #graph_state{ agent_id = AgentId, learning_parameters = LearningParams }, % Schedule periodic maintenance schedule_maintenance(), {ok, State}. handle_call({add_node, NodeId, NodeData}, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Adding node ~p~n", [NodeId]), % Create knowledge node Node = create_knowledge_node(NodeId, NodeData), % Add to graph NewNodes = maps:put(NodeId, Node, State#graph_state.nodes), % Update indices NewNodeIndex = update_node_index(Node, State#graph_state.node_index), NewState = State#graph_state{ nodes = NewNodes, node_index = NewNodeIndex }, {reply, {ok, NodeId}, NewState}; handle_call({add_edge, EdgeId, FromNode, ToNode}, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Adding edge ~p from ~p to ~p~n", [EdgeId, FromNode, ToNode]), % Validate nodes exist case {maps:find(FromNode, State#graph_state.nodes), maps:find(ToNode, State#graph_state.nodes)} of {{ok, _}, {ok, _}} -> % Create knowledge edge Edge = create_knowledge_edge(EdgeId, FromNode, ToNode), % Add to graph NewEdges = maps:put(EdgeId, Edge, State#graph_state.edges), % Update indices NewEdgeIndex = update_edge_index(Edge, State#graph_state.edge_index), NewState = State#graph_state{ edges = NewEdges, edge_index = NewEdgeIndex }, {reply, {ok, EdgeId}, NewState}; _ -> {reply, {error, nodes_not_found}, State} end; handle_call({get_node, NodeId}, _From, State) -> case maps:find(NodeId, State#graph_state.nodes) of {ok, Node} -> % Update access statistics UpdatedNode = Node#knowledge_node{ last_accessed = erlang:system_time(second), access_count = Node#knowledge_node.access_count + 1 }, NewNodes = maps:put(NodeId, UpdatedNode, State#graph_state.nodes), NewState = State#graph_state{nodes = NewNodes}, {reply, {ok, UpdatedNode}, NewState}; error -> {reply, {error, node_not_found}, State} end; handle_call({explore_neighborhood, StartNode, Depth}, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Exploring neighborhood of ~p with depth ~p~n", [StartNode, Depth]), % Perform neighborhood exploration ExplorationResult = explore_node_neighborhood(StartNode, Depth, State), % Record exploration in history ExplorationRecord = #{ type => neighborhood_exploration, start_node => StartNode, depth => Depth, result => ExplorationResult, timestamp => erlang:system_time(second) }, NewHistory = [ExplorationRecord | State#graph_state.exploration_history], NewState = State#graph_state{exploration_history = NewHistory}, {reply, {ok, ExplorationResult}, NewState}; handle_call({find_paths, StartNode, EndNode, MaxDepth}, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Finding paths from ~p to ~p (max depth: ~p)~n", [StartNode, EndNode, MaxDepth]), % Find all paths between nodes Paths = find_all_paths(StartNode, EndNode, MaxDepth, State), {reply, {ok, Paths}, State}; handle_call({discover_patterns, PatternType}, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Discovering patterns of type ~p~n", [PatternType]), % Pattern discovery based on graph structure DiscoveredPatterns = discover_graph_patterns(PatternType, State), % Add discovered patterns to state NewPatterns = maps:merge(State#graph_state.patterns, DiscoveredPatterns), NewState = State#graph_state{patterns = NewPatterns}, {reply, {ok, DiscoveredPatterns}, NewState}; handle_call({suggest_connections, NodeId}, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Suggesting connections for node ~p~n", [NodeId]), % Suggest potential connections based on various heuristics SuggestedConnections = suggest_node_connections(NodeId, State), {reply, {ok, SuggestedConnections}, State}; handle_call({perform_reasoning, ReasoningType, Context}, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Performing ~p reasoning with context ~p~n", [ReasoningType, Context]), % Perform reasoning based on type ReasoningResult = execute_reasoning(ReasoningType, Context, State), % Cache reasoning result CacheKey = {ReasoningType, Context}, NewReasoningCache = maps:put(CacheKey, ReasoningResult, State#graph_state.reasoning_cache), NewState = State#graph_state{reasoning_cache = NewReasoningCache}, {reply, {ok, ReasoningResult}, NewState}; handle_call({generate_hypotheses, Domain}, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Generating hypotheses for domain ~p~n", [Domain]), % Generate hypotheses based on knowledge graph structure and content Hypotheses = generate_domain_hypotheses(Domain, State), {reply, {ok, Hypotheses}, State}; handle_call({validate_hypothesis, Hypothesis, Evidence}, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Validating hypothesis ~p with evidence ~p~n", [Hypothesis, Evidence]), % Validate hypothesis against knowledge graph ValidationResult = validate_hypothesis_against_knowledge(Hypothesis, Evidence, State), {reply, {ok, ValidationResult}, State}; handle_call({measure_semantic_similarity, Node1, Node2}, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Measuring semantic similarity between ~p and ~p~n", [Node1, Node2]), % Calculate semantic similarity using various methods Similarity = calculate_semantic_similarity(Node1, Node2, State), {reply, {ok, Similarity}, State}; handle_call(analyze_graph_structure, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Analyzing graph structure~n"), % Comprehensive graph structure analysis StructureAnalysis = perform_structure_analysis(State), {reply, {ok, StructureAnalysis}, State}; handle_call(compute_graph_metrics, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Computing graph metrics~n"), % Compute various graph metrics Metrics = compute_comprehensive_metrics(State), % Cache metrics NewState = State#graph_state{graph_metrics = Metrics}, {reply, {ok, Metrics}, NewState}; handle_call(consolidate_knowledge, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Consolidating knowledge~n"), % Consolidate related knowledge items ConsolidatedState = perform_knowledge_consolidation(State), {reply, {ok, consolidation_complete}, ConsolidatedState}; handle_call(prune_redundant_knowledge, _From, State) -> io:format("[KNOWLEDGE_GRAPH] Pruning redundant knowledge~n"), % Remove redundant or low-value knowledge PrunedState = perform_knowledge_pruning(State), {reply, {ok, pruning_complete}, PrunedState}; handle_call(_Request, _From, State) -> {reply, {error, unknown_request}, State}. handle_cast({learn_from_interaction, Interaction, Outcome}, State) -> io:format("[KNOWLEDGE_GRAPH] Learning from interaction: ~p -> ~p~n", [Interaction, Outcome]), % Learn from interaction and adapt graph structure NewState = learn_from_interaction_internal(Interaction, Outcome, State), {noreply, NewState}; handle_cast({adapt_structure, AdaptationSignal}, State) -> io:format("[KNOWLEDGE_GRAPH] Adapting structure based on signal: ~p~n", [AdaptationSignal]), % Adapt graph structure based on signal NewState = adapt_graph_structure(AdaptationSignal, State), {noreply, NewState}; handle_cast({evolve_concepts, EvolutionPressure}, State) -> io:format("[KNOWLEDGE_GRAPH] Evolving concepts under pressure: ~p~n", [EvolutionPressure]), % Evolve concepts and abstractions NewState = evolve_concept_structure(EvolutionPressure, State), {noreply, NewState}; handle_cast(_Msg, State) -> {noreply, State}. handle_info(maintenance_cycle, State) -> io:format("[KNOWLEDGE_GRAPH] Performing maintenance cycle~n"), % Perform periodic maintenance MaintenanceState = perform_periodic_maintenance(State), % Schedule next maintenance schedule_maintenance(), {noreply, MaintenanceState}; handle_info(_Info, State) -> {noreply, State}. terminate(_Reason, State) -> io:format("[KNOWLEDGE_GRAPH] Knowledge graph for agent ~p terminating~n", [State#graph_state.agent_id]), % Save knowledge graph state save_knowledge_graph(State), ok. code_change(_OldVsn, State, _Extra) -> {ok, State}. %%==================================================================== %% Internal functions - Node and Edge Creation %%==================================================================== create_knowledge_node(NodeId, NodeData) -> #knowledge_node{ id = NodeId, type = maps:get(type, NodeData, concept), properties = maps:get(properties, NodeData, #{}), content = maps:get(content, NodeData, undefined), confidence = maps:get(confidence, NodeData, 1.0), creation_time = erlang:system_time(second), last_accessed = erlang:system_time(second), source = maps:get(source, NodeData, unknown), tags = maps:get(tags, NodeData, []), metadata = maps:get(metadata, NodeData, #{}) }. create_knowledge_edge(EdgeId, FromNode, ToNode) -> #knowledge_edge{ id = EdgeId, from_node = FromNode, to_node = ToNode, relation_type = generic, weight = 1.0, confidence = 1.0, bidirectional = false, creation_time = erlang:system_time(second), source = unknown, evidence = [], metadata = #{} }. update_node_index(Node, CurrentIndex) -> % Update various indices for fast node lookup TypeIndex = maps:get(by_type, CurrentIndex, #{}), TagIndex = maps:get(by_tag, CurrentIndex, #{}), ContentIndex = maps:get(by_content, CurrentIndex, #{}), % Update type index TypeKey = Node#knowledge_node.type, TypeNodes = maps:get(TypeKey, TypeIndex, []), NewTypeIndex = maps:put(TypeKey, [Node#knowledge_node.id | TypeNodes], TypeIndex), % Update tag index NewTagIndex = lists:foldl(fun(Tag, TagIndexAcc) -> TagNodes = maps:get(Tag, TagIndexAcc, []), maps:put(Tag, [Node#knowledge_node.id | TagNodes], TagIndexAcc) end, TagIndex, Node#knowledge_node.tags), % Return updated index #{ by_type => NewTypeIndex, by_tag => NewTagIndex, by_content => ContentIndex }. update_edge_index(Edge, CurrentIndex) -> % Update edge indices for fast lookup FromIndex = maps:get(from_node, CurrentIndex, #{}), ToIndex = maps:get(to_node, CurrentIndex, #{}), TypeIndex = maps:get(by_type, CurrentIndex, #{}), % Update from_node index FromEdges = maps:get(Edge#knowledge_edge.from_node, FromIndex, []), NewFromIndex = maps:put(Edge#knowledge_edge.from_node, [Edge#knowledge_edge.id | FromEdges], FromIndex), % Update to_node index ToEdges = maps:get(Edge#knowledge_edge.to_node, ToIndex, []), NewToIndex = maps:put(Edge#knowledge_edge.to_node, [Edge#knowledge_edge.id | ToEdges], ToIndex), % Update type index TypeEdges = maps:get(Edge#knowledge_edge.relation_type, TypeIndex, []), NewTypeIndex = maps:put(Edge#knowledge_edge.relation_type, [Edge#knowledge_edge.id | TypeEdges], TypeIndex), #{ from_node => NewFromIndex, to_node => NewToIndex, by_type => NewTypeIndex }. %%==================================================================== %% Internal functions - Graph Exploration %%==================================================================== explore_node_neighborhood(StartNode, Depth, State) -> % Breadth-first exploration of node neighborhood explore_neighborhood_bfs(StartNode, Depth, State, #{}, []). explore_neighborhood_bfs(_StartNode, 0, _State, Visited, Result) -> #{visited => maps:keys(Visited), nodes => Result}; explore_neighborhood_bfs(StartNode, Depth, State, Visited, Result) -> case maps:find(StartNode, Visited) of {ok, _} -> % Already visited #{visited => maps:keys(Visited), nodes => Result}; error -> % Mark as visited NewVisited = maps:put(StartNode, true, Visited), % Get node case maps:find(StartNode, State#graph_state.nodes) of {ok, Node} -> NewResult = [Node | Result], % Get connected nodes ConnectedNodes = get_connected_nodes(StartNode, State), % Recursively explore connected nodes lists:foldl(fun(ConnectedNode, {AccVisited, AccResult}) -> SubResult = explore_neighborhood_bfs(ConnectedNode, Depth - 1, State, AccVisited, AccResult), SubVisited = maps:get(visited, SubResult), SubNodes = maps:get(nodes, SubResult), { maps:merge(AccVisited, maps:from_list([{V, true} || V <- SubVisited])), SubNodes ++ AccResult } end, {NewVisited, NewResult}, ConnectedNodes); error -> #{visited => maps:keys(NewVisited), nodes => Result} end end. get_connected_nodes(NodeId, State) -> % Get all nodes connected to the given node EdgeIndex = State#graph_state.edge_index, FromIndex = maps:get(from_node, EdgeIndex, #{}), ToIndex = maps:get(to_node, EdgeIndex, #{}), % Outgoing edges OutgoingEdgeIds = maps:get(NodeId, FromIndex, []), OutgoingNodes = [get_edge_to_node(EdgeId, State) || EdgeId <- OutgoingEdgeIds], % Incoming edges IncomingEdgeIds = maps:get(NodeId, ToIndex, []), IncomingNodes = [get_edge_from_node(EdgeId, State) || EdgeId <- IncomingEdgeIds], % Return unique connected nodes lists:usort(OutgoingNodes ++ IncomingNodes). get_edge_to_node(EdgeId, State) -> case maps:find(EdgeId, State#graph_state.edges) of {ok, Edge} -> Edge#knowledge_edge.to_node; error -> undefined end. get_edge_from_node(EdgeId, State) -> case maps:find(EdgeId, State#graph_state.edges) of {ok, Edge} -> Edge#knowledge_edge.from_node; error -> undefined end. find_all_paths(StartNode, EndNode, MaxDepth, State) -> % Find all paths between two nodes using depth-limited search find_paths_dfs(StartNode, EndNode, MaxDepth, [StartNode], [], State). find_paths_dfs(_CurrentNode, _EndNode, 0, _CurrentPath, Paths, _State) -> Paths; find_paths_dfs(CurrentNode, EndNode, Depth, CurrentPath, Paths, State) -> if CurrentNode =:= EndNode -> [lists:reverse(CurrentPath) | Paths]; true -> ConnectedNodes = get_connected_nodes(CurrentNode, State), % Avoid cycles ValidNodes = [Node || Node <- ConnectedNodes, not lists:member(Node, CurrentPath)], lists:foldl(fun(NextNode, AccPaths) -> find_paths_dfs(NextNode, EndNode, Depth - 1, [NextNode | CurrentPath], AccPaths, State) end, Paths, ValidNodes) end. %%==================================================================== %% Internal functions - Pattern Discovery %%==================================================================== discover_graph_patterns(PatternType, State) -> case PatternType of structural -> discover_structural_patterns(State); temporal -> discover_temporal_patterns(State); semantic -> discover_semantic_patterns(State); causal -> discover_causal_patterns(State); _ -> #{} end. discover_structural_patterns(State) -> % Discover common structural patterns in the graph Nodes = maps:values(State#graph_state.nodes), Edges = maps:values(State#graph_state.edges), % Find common subgraph patterns TrianglePatterns = find_triangle_patterns(Nodes, Edges), StarPatterns = find_star_patterns(Nodes, Edges), ChainPatterns = find_chain_patterns(Nodes, Edges), #{ triangles => TrianglePatterns, stars => StarPatterns, chains => ChainPatterns }. discover_semantic_patterns(State) -> % Discover patterns based on semantic content Nodes = maps:values(State#graph_state.nodes), % Group nodes by semantic similarity SemanticClusters = cluster_nodes_semantically(Nodes), #{ semantic_clusters => SemanticClusters }. %%==================================================================== %% Internal functions - Reasoning and Inference %%==================================================================== execute_reasoning(ReasoningType, Context, State) -> case ReasoningType of deductive -> perform_deductive_reasoning(Context, State); inductive -> perform_inductive_reasoning(Context, State); abductive -> perform_abductive_reasoning(Context, State); analogical -> perform_analogical_reasoning(Context, State); causal -> perform_causal_reasoning(Context, State); _ -> #{error => unsupported_reasoning_type} end. perform_deductive_reasoning(Context, State) -> % Deductive reasoning using graph structure and rules Premises = maps:get(premises, Context, []), Rules = State#graph_state.inference_rules, % Apply rules to premises Conclusions = apply_inference_rules(Premises, Rules, State), #{ reasoning_type => deductive, premises => Premises, conclusions => Conclusions, confidence => calculate_reasoning_confidence(Conclusions) }. perform_inductive_reasoning(Context, State) -> % Inductive reasoning to generalize from specific instances Examples = maps:get(examples, Context, []), % Find common patterns among examples CommonPatterns = extract_common_patterns(Examples, State), % Generate generalizations Generalizations = generate_generalizations(CommonPatterns, State), #{ reasoning_type => inductive, examples => Examples, patterns => CommonPatterns, generalizations => Generalizations }. perform_analogical_reasoning(Context, State) -> % Analogical reasoning between domains SourceDomain = maps:get(source_domain, Context), TargetDomain = maps:get(target_domain, Context), % Find structural similarities StructuralMappings = find_structural_analogies(SourceDomain, TargetDomain, State), % Generate analogical inferences AnalogicalInferences = generate_analogical_inferences(StructuralMappings, State), #{ reasoning_type => analogical, source_domain => SourceDomain, target_domain => TargetDomain, mappings => StructuralMappings, inferences => AnalogicalInferences }. %%==================================================================== %% Internal functions - Graph Analysis %%==================================================================== perform_structure_analysis(State) -> Nodes = maps:values(State#graph_state.nodes), Edges = maps:values(State#graph_state.edges), NodeCount = length(Nodes), EdgeCount = length(Edges), % Calculate basic metrics Density = case NodeCount of 0 -> 0.0; N when N > 1 -> EdgeCount / (N * (N - 1) / 2); _ -> 0.0 end, % Analyze connectivity ConnectivityAnalysis = analyze_connectivity(State), % Find central nodes CentralNodes = find_central_nodes(State), #{ node_count => NodeCount, edge_count => EdgeCount, density => Density, connectivity => ConnectivityAnalysis, central_nodes => CentralNodes, analysis_timestamp => erlang:system_time(second) }. analyze_connectivity(State) -> % Analyze graph connectivity properties Components = find_connected_components(State), LargestComponent = find_largest_component(Components), #{ component_count => length(Components), largest_component_size => length(LargestComponent), components => Components }. find_connected_components(State) -> % Find all connected components using DFS Nodes = maps:keys(State#graph_state.nodes), find_components_dfs(Nodes, [], State). find_components_dfs([], Components, _State) -> Components; find_components_dfs([Node | RestNodes], Components, State) -> % Check if node is already in a component case is_node_in_components(Node, Components) of true -> find_components_dfs(RestNodes, Components, State); false -> % Start new component from this node Component = explore_component(Node, [], State), find_components_dfs(RestNodes, [Component | Components], State) end. explore_component(Node, Visited, State) -> case lists:member(Node, Visited) of true -> Visited; false -> NewVisited = [Node | Visited], ConnectedNodes = get_connected_nodes(Node, State), lists:foldl(fun(ConnectedNode, AccVisited) -> explore_component(ConnectedNode, AccVisited, State) end, NewVisited, ConnectedNodes) end. %%==================================================================== %% Internal functions - Learning and Adaptation %%==================================================================== learn_from_interaction_internal(Interaction, Outcome, State) -> % Learn from interaction and adapt knowledge graph % Extract knowledge from interaction NewKnowledge = extract_knowledge_from_interaction(Interaction, Outcome), % Update graph structure UpdatedState = integrate_new_knowledge(NewKnowledge, State), % Adjust edge weights based on outcome adjust_edge_weights_from_outcome(Outcome, UpdatedState). extract_knowledge_from_interaction(Interaction, Outcome) -> % Extract actionable knowledge from interaction #{ interaction_type => maps:get(type, Interaction, unknown), context => maps:get(context, Interaction, #{}), outcome_type => maps:get(type, Outcome, unknown), outcome_quality => maps:get(quality, Outcome, neutral), timestamp => erlang:system_time(second) }. integrate_new_knowledge(_Knowledge, State) -> % Integrate new knowledge into the graph % This is a simplified implementation State. adapt_graph_structure(AdaptationSignal, State) -> % Adapt graph structure based on signal case maps:get(type, AdaptationSignal) of strengthen_connections -> strengthen_connection_weights(AdaptationSignal, State); weaken_connections -> weaken_connection_weights(AdaptationSignal, State); add_new_connections -> add_suggested_connections(AdaptationSignal, State); remove_weak_connections -> remove_weak_connections(AdaptationSignal, State); _ -> State end. %%==================================================================== %% Internal functions - Utility and Helper Functions %%==================================================================== generate_graph_id() -> iolist_to_binary(io_lib:format("knowledge_graph_~p", [erlang:system_time(microsecond)])). schedule_maintenance() -> Interval = 300000, % 5 minutes erlang:send_after(Interval, self(), maintenance_cycle). perform_periodic_maintenance(State) -> % Perform periodic maintenance tasks % 1. Update access statistics and decay unused connections DecayedState = decay_unused_connections(State), % 2. Consolidate similar concepts ConsolidatedState = auto_consolidate_concepts(DecayedState), % 3. Prune low-confidence knowledge PrunedState = auto_prune_low_confidence(ConsolidatedState), % 4. Update graph metrics MetricsState = update_cached_metrics(PrunedState), MetricsState. save_knowledge_graph(_State) -> % Save knowledge graph to persistent storage ok. % Placeholder implementations for complex functions suggest_node_connections(_NodeId, _State) -> []. generate_domain_hypotheses(_Domain, _State) -> []. validate_hypothesis_against_knowledge(_Hypothesis, _Evidence, _State) -> #{valid => true}. calculate_semantic_similarity(_Node1, _Node2, _State) -> 0.5. compute_comprehensive_metrics(_State) -> #{}. perform_knowledge_consolidation(State) -> State. perform_knowledge_pruning(State) -> State. evolve_concept_structure(_EvolutionPressure, State) -> State. find_triangle_patterns(_Nodes, _Edges) -> []. find_star_patterns(_Nodes, _Edges) -> []. find_chain_patterns(_Nodes, _Edges) -> []. cluster_nodes_semantically(_Nodes) -> []. apply_inference_rules(_Premises, _Rules, _State) -> []. calculate_reasoning_confidence(_Conclusions) -> 0.8. extract_common_patterns(_Examples, _State) -> []. generate_generalizations(_Patterns, _State) -> []. find_structural_analogies(_SourceDomain, _TargetDomain, _State) -> []. generate_analogical_inferences(_Mappings, _State) -> []. find_central_nodes(_State) -> []. find_largest_component(Components) -> lists:max(Components). is_node_in_components(_Node, _Components) -> false. adjust_edge_weights_from_outcome(_Outcome, State) -> State. strengthen_connection_weights(_Signal, State) -> State. weaken_connection_weights(_Signal, State) -> State. add_suggested_connections(_Signal, State) -> State. remove_weak_connections(_Signal, State) -> State. decay_unused_connections(State) -> State. auto_consolidate_concepts(State) -> State. auto_prune_low_confidence(State) -> State. update_cached_metrics(State) -> State. discover_temporal_patterns(_State) -> #{}. discover_causal_patterns(_State) -> #{}. perform_causal_reasoning(_Context, _State) -> #{}. perform_abductive_reasoning(_Context, _State) -> #{}.