agents

-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) -> #{}.