defmodule Automata.DistributedCognition.BeliefArchitecture.BeliefPropagation do
  @moduledoc """
  Belief Propagation System for Decentralized Belief Architecture

  This module implements mechanisms for propagating and updating beliefs across
  distributed agents:
  - Asynchronous belief updates with convergence guarantees
  - Belief conflict resolution with provable properties
  - Uncertainty representation with proper aggregation
  """

  alias Automata.DistributedCognition.BeliefArchitecture.BeliefPropagation.{
    AsyncUpdates,
    ConflictResolution,
    UncertaintyRepresentation
  }

  defmodule BeliefAtom do
    @moduledoc """
    Structure representing an atomic belief statement
    """

    @type t :: %__MODULE__{
            id: String.t(),
            content: any(),
            source: atom() | pid(),
            confidence: float(),
            timestamp: DateTime.t(),
            metadata: map(),
            tags: list(atom())
          }

    defstruct [
      :id,
      :content,
      :source,
      :confidence,
      :timestamp,
      :metadata,
      :tags
    ]

    @doc """
    Creates a new belief atom
    """
    @spec new(any(), atom() | pid(), float(), keyword()) :: t()
    def new(content, source, confidence, options \\ []) do
      %__MODULE__{
        id: generate_id(content, source),
        content: content,
        source: source,
        confidence: confidence,
        timestamp: DateTime.utc_now(),
        metadata: Keyword.get(options, :metadata, %{}),
        tags: Keyword.get(options, :tags, [])
      }
    end

    @doc """
    Generates a unique ID for a belief
    """
    @spec generate_id(any(), atom() | pid()) :: String.t()
    def generate_id(content, source) do
      content_hash = :erlang.phash2(content, 1_000_000)
      source_str = if is_atom(source), do: Atom.to_string(source), else: inspect(source)
      source_hash = :erlang.phash2(source_str, 1_000_000)
      timestamp = System.system_time(:millisecond)
      "belief_#{content_hash}_#{source_hash}_#{timestamp}"
    end

    @doc """
    Updates belief confidence
    """
    @spec update_confidence(t(), float()) :: t()
    def update_confidence(belief, new_confidence) do
      %__MODULE__{belief | confidence: new_confidence, timestamp: DateTime.utc_now()}
    end

    @doc """
    Merges additional metadata into belief
    """
    @spec add_metadata(t(), map()) :: t()
    def add_metadata(belief, new_metadata) do
      updated_metadata = Map.merge(belief.metadata, new_metadata)
      %__MODULE__{belief | metadata: updated_metadata, timestamp: DateTime.utc_now()}
    end

    @doc """
    Adds tags to belief
    """
    @spec add_tags(t(), list(atom())) :: t()
    def add_tags(belief, new_tags) do
      updated_tags = (belief.tags ++ new_tags) |> Enum.uniq()
      %__MODULE__{belief | tags: updated_tags, timestamp: DateTime.utc_now()}
    end
  end

  defmodule BeliefSet do
    @moduledoc """
    Structure representing a set of beliefs held by an agent
    """

    @type t :: %__MODULE__{
            beliefs: map(),
            agent_id: atom() | pid(),
            metadata: map(),
            last_updated: DateTime.t()
          }

    defstruct [
      :beliefs,
      :agent_id,
      :metadata,
      :last_updated
    ]

    @doc """
    Creates a new empty belief set
    """
    @spec new(atom() | pid(), keyword()) :: t()
    def new(agent_id, options \\ []) do
      %__MODULE__{
        beliefs: %{},
        agent_id: agent_id,
        metadata: Keyword.get(options, :metadata, %{}),
        last_updated: DateTime.utc_now()
      }
    end

    @doc """
    Adds a belief to the belief set
    """
    @spec add_belief(t(), BeliefAtom.t()) :: t()
    def add_belief(belief_set, belief) do
      updated_beliefs = Map.put(belief_set.beliefs, belief.id, belief)
      %__MODULE__{belief_set | beliefs: updated_beliefs, last_updated: DateTime.utc_now()}
    end

    @doc """
    Removes a belief from the belief set
    """
    @spec remove_belief(t(), String.t()) :: t()
    def remove_belief(belief_set, belief_id) do
      updated_beliefs = Map.delete(belief_set.beliefs, belief_id)
      %__MODULE__{belief_set | beliefs: updated_beliefs, last_updated: DateTime.utc_now()}
    end

    @doc """
    Updates a belief in the belief set
    """
    @spec update_belief(t(), BeliefAtom.t()) :: t()
    def update_belief(belief_set, updated_belief) do
      updated_beliefs = Map.put(belief_set.beliefs, updated_belief.id, updated_belief)
      %__MODULE__{belief_set | beliefs: updated_beliefs, last_updated: DateTime.utc_now()}
    end

    @doc """
    Retrieves a belief by ID
    """
    @spec get_belief(t(), String.t()) :: BeliefAtom.t() | nil
    def get_belief(belief_set, belief_id) do
      Map.get(belief_set.beliefs, belief_id)
    end

    @doc """
    Filters beliefs by a predicate function
    """
    @spec filter_beliefs(t(), (BeliefAtom.t() -> boolean())) :: list(BeliefAtom.t())
    def filter_beliefs(belief_set, predicate) do
      belief_set.beliefs
      |> Map.values()
      |> Enum.filter(predicate)
    end

    @doc """
    Finds conflicts between beliefs in this belief set
    """
    @spec find_conflicts(t()) :: list({BeliefAtom.t(), BeliefAtom.t()})
    def find_conflicts(belief_set) do
      beliefs = Map.values(belief_set.beliefs)
      
      # Compare each pair of beliefs for conflicts
      for belief1 <- beliefs, belief2 <- beliefs, belief1.id != belief2.id do
        if ConflictResolution.are_conflicting(belief1, belief2) do
          {belief1, belief2}
        else
          nil
        end
      end
      |> Enum.reject(&is_nil/1)
    end

    @doc """
    Merges two belief sets, handling conflicts using the provided resolution strategy
    """
    @spec merge(t(), t(), atom()) :: t()
    def merge(belief_set1, belief_set2, conflict_strategy \\ :probabilistic) do
      # Start with belief_set1
      result_set = belief_set1
      
      # Iterate through belief_set2, adding or merging beliefs
      Enum.reduce(belief_set2.beliefs, result_set, fn {belief_id, belief}, acc ->
        case get_belief(acc, belief_id) do
          nil ->
            # Belief doesn't exist in set1, simply add it
            add_belief(acc, belief)
            
          existing_belief ->
            # Belief exists, check for conflict
            if ConflictResolution.are_conflicting(existing_belief, belief) do
              # Resolve conflict
              resolved_belief = ConflictResolution.resolve_conflict(
                existing_belief, 
                belief, 
                conflict_strategy
              )
              update_belief(acc, resolved_belief)
            else
              # Not conflicting, keep the highest confidence one
              if belief.confidence > existing_belief.confidence do
                update_belief(acc, belief)
              else
                acc
              end
            end
        end
      end)
      |> Map.put(:last_updated, DateTime.utc_now())
    end

    @doc """
    Calculates the consistency score of the belief set (0-1)
    """
    @spec consistency_score(t()) :: float()
    def consistency_score(belief_set) do
      conflicts = find_conflicts(belief_set)
      belief_count = map_size(belief_set.beliefs)
      
      if belief_count <= 1 do
        1.0  # No conflicts possible with 0 or 1 beliefs
      else
        max_possible_conflicts = div(belief_count * (belief_count - 1), 2)
        conflict_count = length(conflicts)
        
        1.0 - (conflict_count / max_possible_conflicts)
      end
    end
  end

  defmodule AsyncUpdates do
    @moduledoc """
    Provides asynchronous belief update mechanisms with convergence guarantees
    """

    @doc """
    Propagates a belief update to a list of agents
    """
    @spec propagate_belief(BeliefAtom.t(), list(pid()), keyword()) :: map()
    def propagate_belief(belief, target_agents, options \\ []) do
      # Options
      propagation_mode = Keyword.get(options, :mode, :async)
      timeout = Keyword.get(options, :timeout, 5000)
      
      case propagation_mode do
        :async ->
          # Asynchronous propagation (fire and forget)
          Enum.each(target_agents, fn agent ->
            send(agent, {:belief_update, belief})
          end)
          
          %{
            status: :initiated,
            targets: length(target_agents),
            timestamp: DateTime.utc_now()
          }
          
        :sync ->
          # Synchronous propagation (wait for acknowledgements)
          results = Enum.map(target_agents, fn agent ->
            ref = Process.monitor(agent)
            send(agent, {:belief_update, belief, self(), ref})
            
            receive do
              {:belief_ack, ^ref, status} ->
                Process.demonitor(ref, [:flush])
                {agent, status}
                
              {:DOWN, ^ref, _, _, reason} ->
                {agent, {:error, reason}}
            after
              timeout ->
                Process.demonitor(ref, [:flush])
                {agent, {:error, :timeout}}
            end
          end)
          
          successful = Enum.count(results, fn {_, status} -> status == :accepted end)
          
          %{
            status: :completed,
            targets: length(target_agents),
            successful: successful,
            results: results,
            timestamp: DateTime.utc_now()
          }
      end
    end

    @doc """
    Processes an incoming belief update
    """
    @spec process_belief_update(BeliefAtom.t(), BeliefSet.t(), keyword()) :: {BeliefSet.t(), :accepted | :rejected}
    def process_belief_update(belief, belief_set, options \\ []) do
      # Options
      acceptance_threshold = Keyword.get(options, :acceptance_threshold, 0.5)
      
      # Check if we have an existing belief with the same ID
      case BeliefSet.get_belief(belief_set, belief.id) do
        nil ->
          # New belief, add if confidence exceeds threshold
          if belief.confidence >= acceptance_threshold do
            {BeliefSet.add_belief(belief_set, belief), :accepted}
          else
            {belief_set, :rejected}
          end
          
        existing_belief ->
          # Existing belief, check if update is more recent
          if DateTime.compare(belief.timestamp, existing_belief.timestamp) == :gt do
            # Newer belief, check for conflict
            if ConflictResolution.are_conflicting(existing_belief, belief) do
              # Conflict, resolve using configured strategy
              conflict_strategy = Keyword.get(options, :conflict_strategy, :probabilistic)
              resolved_belief = ConflictResolution.resolve_conflict(
                existing_belief, 
                belief, 
                conflict_strategy
              )
              
              {BeliefSet.update_belief(belief_set, resolved_belief), :accepted}
            else
              # No conflict, use the belief with higher confidence
              if belief.confidence > existing_belief.confidence do
                {BeliefSet.update_belief(belief_set, belief), :accepted}
              else
                {belief_set, :rejected}
              end
            end
          else
            # Older belief, reject update
            {belief_set, :rejected}
          end
      end
    end

    @doc """
    Synchronizes belief sets between agents to ensure eventual consistency
    """
    @spec synchronize_beliefs(BeliefSet.t(), BeliefSet.t(), keyword()) :: {BeliefSet.t(), BeliefSet.t()}
    def synchronize_beliefs(belief_set1, belief_set2, options \\ []) do
      # Options
      conflict_strategy = Keyword.get(options, :conflict_strategy, :probabilistic)
      
      # Create merged sets in both directions
      merged1 = BeliefSet.merge(belief_set1, belief_set2, conflict_strategy)
      merged2 = BeliefSet.merge(belief_set2, belief_set1, conflict_strategy)
      
      {merged1, merged2}
    end

    @doc """
    Verifies if beliefs have converged across a set of belief sets
    """
    @spec verify_convergence(list(BeliefSet.t()), keyword()) :: {boolean(), float()}
    def verify_convergence(belief_sets, options \\ []) do
      # Options
      convergence_threshold = Keyword.get(options, :convergence_threshold, 0.95)
      
      # Early return for empty or single belief set
      if length(belief_sets) <= 1 do
        {true, 1.0}
      else
        # Extract all beliefs with IDs
        all_belief_ids = 
          belief_sets
          |> Enum.flat_map(fn set -> Map.keys(set.beliefs) end)
          |> Enum.uniq()
        
        # Count how many sets contain each belief ID
        belief_counts = Enum.map(all_belief_ids, fn id ->
          count = Enum.count(belief_sets, fn set -> Map.has_key?(set.beliefs, id) end)
          {id, count}
        end)
        |> Map.new()
        
        # Calculate convergence score
        total_possible = length(belief_sets) * length(all_belief_ids)
        
        if total_possible == 0 do
          {true, 1.0}  # No beliefs at all, perfect convergence
        else
          actual_sum = Enum.sum(Map.values(belief_counts))
          convergence_score = actual_sum / total_possible
          
          {convergence_score >= convergence_threshold, convergence_score}
        end
      end
    end

    @doc """
    Detects partition in the belief propagation network
    """
    @spec detect_network_partition(list(BeliefSet.t()), keyword()) :: {boolean(), list(list(BeliefSet.t()))}
    def detect_network_partition(belief_sets, options \\ []) do
      # Options
      partition_threshold = Keyword.get(options, :partition_threshold, 0.3)
      
      if length(belief_sets) <= 1 do
        {false, [belief_sets]}  # No partition possible with 0 or 1 sets
      else
        # Create similarity matrix between belief sets
        similarity_matrix = 
          for set1 <- belief_sets do
            for set2 <- belief_sets do
              calculate_belief_set_similarity(set1, set2)
            end
          end
        
        # Use hierarchical clustering to identify groups
        clusters = hierarchical_clustering(belief_sets, similarity_matrix, partition_threshold)
        
        # Determine if partitioned (more than one cluster)
        {length(clusters) > 1, clusters}
      end
    end

    @doc """
    Calculates similarity between two belief sets (0-1)
    """
    @spec calculate_belief_set_similarity(BeliefSet.t(), BeliefSet.t()) :: float()
    defp calculate_belief_set_similarity(set1, set2) do
      # Get all unique belief IDs from both sets
      all_ids = MapSet.union(
        MapSet.new(Map.keys(set1.beliefs)),
        MapSet.new(Map.keys(set2.beliefs))
      )
      
      if Enum.empty?(all_ids) do
        1.0  # Both empty, perfectly similar
      else
        # Count beliefs in both sets (intersection)
        common_ids = MapSet.intersection(
          MapSet.new(Map.keys(set1.beliefs)),
          MapSet.new(Map.keys(set2.beliefs))
        )
        
        # Jaccard similarity: |A ∩ B| / |A ∪ B|
        MapSet.size(common_ids) / MapSet.size(all_ids)
      end
    end

    @doc """
    Simple hierarchical clustering algorithm
    """
    @spec hierarchical_clustering(list(BeliefSet.t()), list(list(float())), float()) :: list(list(BeliefSet.t()))
    defp hierarchical_clustering(belief_sets, similarity_matrix, threshold) do
      # Initialize each belief set as its own cluster
      clusters = Enum.map(belief_sets, fn set -> [set] end)
      
      # Helper to find pair with highest similarity
      find_most_similar = fn clusters, sim_matrix ->
        max_sim = -1
        max_pair = {-1, -1}
        
        # Compare each pair of clusters
        result = for i <- 0..(length(clusters) - 1), j <- 0..(length(clusters) - 1), i < j, reduce: {max_sim, max_pair} do
          {max_sim, max_pair} ->
            # Calculate average similarity between clusters
            avg_sim = for ci <- clusters |> Enum.at(i) |> Enum.with_index(),
                          cj <- clusters |> Enum.at(j) |> Enum.with_index() do
              {_, i_idx} = ci
              {_, j_idx} = cj
              Enum.at(Enum.at(sim_matrix, i_idx), j_idx)
            end |> Enum.sum() / (length(Enum.at(clusters, i)) * length(Enum.at(clusters, j)))
            
            if avg_sim > max_sim do
              {avg_sim, {i, j}}
            else
              {max_sim, max_pair}
            end
        end
        
        result
      end
      
      # Iteratively merge clusters until similarity falls below threshold
      iterate_clustering = fn clusters, f ->
        {max_sim, {i, j}} = find_most_similar.(clusters, similarity_matrix)
        
        if max_sim >= threshold and i >= 0 and j >= 0 do
          # Merge clusters
          cluster_i = Enum.at(clusters, i)
          cluster_j = Enum.at(clusters, j)
          merged = cluster_i ++ cluster_j
          
          new_clusters = 
            clusters
            |> List.delete_at(max(i, j))
            |> List.delete_at(min(i, j))
            |> List.insert_at(0, merged)
          
          f.(new_clusters, f)
        else
          clusters
        end
      end
      
      iterate_clustering.(clusters, iterate_clustering)
    end
  end

  defmodule ConflictResolution do
    @moduledoc """
    Provides mechanisms for resolving conflicts between beliefs
    """

    @doc """
    Determines if two beliefs are in conflict
    """
    @spec are_conflicting(BeliefAtom.t(), BeliefAtom.t()) :: boolean()
    def are_conflicting(belief1, belief2) do
      # Simple implementation: beliefs are conflicting if they have the same
      # structure but different content
      # In a real system, this would use domain-specific conflict detection
      
      # Extract structure of content (the "shape" of the belief)
      structure1 = extract_structure(belief1.content)
      structure2 = extract_structure(belief2.content)
      
      if structure1 == structure2 do
        # Same structure, check for content difference
        belief1.content != belief2.content
      else
        # Different structures, not conflicting
        false
      end
    end

    @doc """
    Extracts the structure (shape) of a belief content
    """
    @spec extract_structure(any()) :: any()
    defp extract_structure(content) when is_map(content) do
      Map.new(content, fn {k, v} -> {k, extract_structure(v)} end)
    end
    
    defp extract_structure(content) when is_list(content) do
      if Keyword.keyword?(content) do
        # Keyword list, preserve keys
        Keyword.new(content, fn {k, v} -> {k, extract_structure(v)} end)
      else
        # Regular list, just keep the structure pattern
        Enum.map(content, fn _ -> :item end)
      end
    end
    
    defp extract_structure(content) when is_tuple(content) do
      # Convert to list, extract structure, convert back to tuple
      content
      |> Tuple.to_list()
      |> Enum.map(fn _ -> :item end)
      |> List.to_tuple()
    end
    
    defp extract_structure(_content) do
      # For simple values, just return a placeholder
      :value
    end

    @doc """
    Resolves a conflict between two beliefs
    """
    @spec resolve_conflict(BeliefAtom.t(), BeliefAtom.t(), atom()) :: BeliefAtom.t()
    def resolve_conflict(belief1, belief2, strategy \\ :probabilistic) do
      case strategy do
        :highest_confidence ->
          # Simply take the belief with higher confidence
          if belief1.confidence >= belief2.confidence do
            belief1
          else
            belief2
          end
          
        :newest ->
          # Take the most recent belief
          case DateTime.compare(belief1.timestamp, belief2.timestamp) do
            :gt -> belief1
            _ -> belief2
          end
          
        :probabilistic ->
          # Probabilistically choose based on relative confidences
          total_confidence = belief1.confidence + belief2.confidence
          prob_belief1 = belief1.confidence / total_confidence
          
          if :rand.uniform() < prob_belief1 do
            belief1
          else
            belief2
          end
          
        :authority ->
          # Determine based on source authority
          # This is a placeholder - in a real system, we'd have a source authority ranking
          if authority_level(belief1.source) >= authority_level(belief2.source) do
            belief1
          else
            belief2
          end
          
        :merge ->
          # Try to create a merged belief
          # This is a placeholder - in a real system, we'd have domain-specific merge logic
          merged_content = merge_contents(belief1.content, belief2.content)
          
          # Create a new belief with the merged content
          %BeliefAtom{
            id: BeliefAtom.generate_id(merged_content, belief1.source),
            content: merged_content,
            source: belief1.source,  # Keep original source for traceability
            confidence: (belief1.confidence + belief2.confidence) / 2,  # Average confidence
            timestamp: DateTime.utc_now(),
            metadata: Map.merge(belief1.metadata, belief2.metadata),
            tags: (belief1.tags ++ belief2.tags) |> Enum.uniq()
          }
          
        _ ->
          # Default to highest confidence strategy
          if belief1.confidence >= belief2.confidence do
            belief1
          else
            belief2
          end
      end
    end

    @doc """
    Determines the authority level of a source (placeholder implementation)
    """
    @spec authority_level(atom() | pid()) :: integer()
    defp authority_level(source) do
      # In a real system, we'd have a proper authority ranking
      # For now, return a placeholder value
      5
    end

    @doc """
    Attempts to merge the contents of two conflicting beliefs (placeholder implementation)
    """
    @spec merge_contents(any(), any()) :: any()
    defp merge_contents(content1, content2) do
      # In a real system, this would have domain-specific merge logic
      # For simple types, prefer content1
      content1
    end

    @doc """
    Validates that a belief set has no internal conflicts
    """
    @spec validate_belief_set(BeliefSet.t()) :: {:ok, BeliefSet.t()} | {:error, list({BeliefAtom.t(), BeliefAtom.t()})}
    def validate_belief_set(belief_set) do
      conflicts = BeliefSet.find_conflicts(belief_set)
      
      if Enum.empty?(conflicts) do
        {:ok, belief_set}
      else
        {:error, conflicts}
      end
    end

    @doc """
    Resolves all conflicts in a belief set
    """
    @spec resolve_all_conflicts(BeliefSet.t(), atom()) :: BeliefSet.t()
    def resolve_all_conflicts(belief_set, strategy \\ :probabilistic) do
      conflicts = BeliefSet.find_conflicts(belief_set)
      
      # Iteratively resolve each conflict
      Enum.reduce(conflicts, belief_set, fn {belief1, belief2}, acc_set ->
        resolved = resolve_conflict(belief1, belief2, strategy)
        
        # Update the belief set by removing both conflicting beliefs and adding the resolved one
        acc_set
        |> BeliefSet.remove_belief(belief1.id)
        |> BeliefSet.remove_belief(belief2.id)
        |> BeliefSet.add_belief(resolved)
      end)
    end
  end

  defmodule UncertaintyRepresentation do
    @moduledoc """
    Provides mechanisms for representing and reasoning with uncertain beliefs
    """

    @type uncertainty_type :: :probabilistic | :fuzzy | :dempster_shafer | :possibilistic

    @doc """
    Creates a belief with uncertainty representation
    """
    @spec create_uncertain_belief(any(), atom() | pid(), uncertainty_type(), map(), keyword()) :: BeliefAtom.t()
    def create_uncertain_belief(content, source, uncertainty_type, uncertainty_values, options \\ []) do
      # Calculate single confidence value based on uncertainty type and values
      confidence = calculate_confidence(uncertainty_type, uncertainty_values)
      
      # Create metadata with uncertainty information
      uncertainty_metadata = %{
        uncertainty_type: uncertainty_type,
        uncertainty_values: uncertainty_values
      }
      
      # Merge with provided metadata
      metadata = Map.merge(
        Keyword.get(options, :metadata, %{}),
        uncertainty_metadata
      )
      
      # Create the belief
      BeliefAtom.new(
        content,
        source,
        confidence,
        [metadata: metadata, tags: Keyword.get(options, :tags, [])]
      )
    end

    @doc """
    Calculates confidence value based on uncertainty representation
    """
    @spec calculate_confidence(uncertainty_type(), map()) :: float()
    defp calculate_confidence(uncertainty_type, values) do
      case uncertainty_type do
        :probabilistic ->
          # For probabilistic, confidence is directly the probability
          Map.get(values, :probability, 0.5)
          
        :fuzzy ->
          # For fuzzy, confidence is the membership degree
          Map.get(values, :membership, 0.5)
          
        :dempster_shafer ->
          # For Dempster-Shafer, confidence is belief (not plausibility)
          Map.get(values, :belief, 0.5)
          
        :possibilistic ->
          # For possibilistic, confidence is derived from necessity
          Map.get(values, :necessity, 0.5)
          
        _ ->
          # Default value
          0.5
      end
    end

    @doc """
    Updates uncertainty values for a belief
    """
    @spec update_uncertainty(BeliefAtom.t(), map()) :: BeliefAtom.t()
    def update_uncertainty(belief, new_values) do
      # Get current uncertainty type and values
      uncertainty_type = get_in(belief.metadata, [:uncertainty_type])
      current_values = get_in(belief.metadata, [:uncertainty_values]) || %{}
      
      # Merge with new values
      updated_values = Map.merge(current_values, new_values)
      
      # Update the confidence based on new values
      new_confidence = calculate_confidence(uncertainty_type, updated_values)
      
      # Update belief metadata and confidence
      updated_metadata = put_in(belief.metadata, [:uncertainty_values], updated_values)
      
      %BeliefAtom{
        belief |
        confidence: new_confidence,
        metadata: updated_metadata,
        timestamp: DateTime.utc_now()
      }
    end

    @doc """
    Aggregates multiple uncertain beliefs about the same content
    """
    @spec aggregate_beliefs(list(BeliefAtom.t()), atom()) :: BeliefAtom.t()
    def aggregate_beliefs(beliefs, method \\ :weighted_average) do
      if Enum.empty?(beliefs) do
        raise ArgumentError, "Cannot aggregate empty list of beliefs"
      end
      
      # Use the first belief as the template
      [first | rest] = beliefs
      
      case method do
        :weighted_average ->
          # Calculate weighted average based on individual confidences
          total_weight = Enum.sum(Enum.map(beliefs, & &1.confidence))
          
          if total_weight == 0 do
            # All zero confidence, use simple average
            aggregate_beliefs(beliefs, :average)
          else
            # Calculate weighted uncertainty values
            uncertainty_type = get_in(first.metadata, [:uncertainty_type])
            all_values = Enum.map(beliefs, & get_in(&1.metadata, [:uncertainty_values]) || %{})
            
            weighted_values = compute_weighted_average_values(all_values, beliefs, total_weight)
            
            # Create a new belief with aggregated values
            create_uncertain_belief(
              first.content,
              first.source,
              uncertainty_type,
              weighted_values,
              [
                tags: Enum.flat_map(beliefs, & &1.tags) |> Enum.uniq(),
                metadata: %{aggregation_method: :weighted_average, source_beliefs: Enum.map(beliefs, & &1.id)}
              ]
            )
          end
          
        :average ->
          # Simple average of uncertainty values
          uncertainty_type = get_in(first.metadata, [:uncertainty_type])
          all_values = Enum.map(beliefs, & get_in(&1.metadata, [:uncertainty_values]) || %{})
          
          # Get all keys across all values
          all_keys = all_values 
                      |> Enum.flat_map(&Map.keys/1) 
                      |> Enum.uniq()
          
          # Calculate average for each key
          avg_values = Map.new(all_keys, fn key ->
            values = Enum.map(all_values, &Map.get(&1, key, 0))
            avg = Enum.sum(values) / length(values)
            {key, avg}
          end)
          
          # Create a new belief with averaged values
          create_uncertain_belief(
            first.content,
            first.source,
            uncertainty_type,
            avg_values,
            [
              tags: Enum.flat_map(beliefs, & &1.tags) |> Enum.uniq(),
              metadata: %{aggregation_method: :average, source_beliefs: Enum.map(beliefs, & &1.id)}
            ]
          )
          
        :dempster_shafer ->
          # Apply Dempster-Shafer combination rule
          # This is a simplified version - a real implementation would be more complex
          uncertainty_type = :dempster_shafer
          all_values = Enum.map(beliefs, & get_in(&1.metadata, [:uncertainty_values]) || %{})
          
          # Apply combination rule (simplified)
          combined_values = %{
            belief: compute_ds_belief(all_values),
            plausibility: compute_ds_plausibility(all_values)
          }
          
          # Create a new belief with combined values
          create_uncertain_belief(
            first.content,
            first.source,
            uncertainty_type,
            combined_values,
            [
              tags: Enum.flat_map(beliefs, & &1.tags) |> Enum.uniq(),
              metadata: %{aggregation_method: :dempster_shafer, source_beliefs: Enum.map(beliefs, & &1.id)}
            ]
          )
          
        :max_confidence ->
          # Simply take the belief with highest confidence
          max_belief = Enum.max_by(beliefs, & &1.confidence)
          max_belief
          
        _ ->
          # Default to weighted average
          aggregate_beliefs(beliefs, :weighted_average)
      end
    end

    @doc """
    Computes weighted average of uncertainty values
    """
    @spec compute_weighted_average_values(list(map()), list(BeliefAtom.t()), float()) :: map()
    defp compute_weighted_average_values(all_values, beliefs, total_weight) do
      # Get all keys across all values
      all_keys = all_values 
                  |> Enum.flat_map(&Map.keys/1) 
                  |> Enum.uniq()
      
      # Calculate weighted average for each key
      Map.new(all_keys, fn key ->
        weighted_sum = Enum.zip(all_values, beliefs)
                        |> Enum.map(fn {values, belief} ->
                            Map.get(values, key, 0) * belief.confidence
                          end)
                        |> Enum.sum()
        
        {key, weighted_sum / total_weight}
      end)
    end

    @doc """
    Computes belief value using Dempster-Shafer combination
    """
    @spec compute_ds_belief(list(map())) :: float()
    defp compute_ds_belief(all_values) do
      # Extract belief values or use 0.5 as default
      belief_values = Enum.map(all_values, &Map.get(&1, :belief, 0.5))
      
      # Simplified combination (for a proper implementation, we would
      # need to handle mass functions and basic probability assignments)
      Enum.reduce(belief_values, 1, fn bel, acc -> acc * bel end)
    end

    @doc """
    Computes plausibility value using Dempster-Shafer combination
    """
    @spec compute_ds_plausibility(list(map())) :: float()
    defp compute_ds_plausibility(all_values) do
      # Extract plausibility values or use 0.5 as default
      plausibility_values = Enum.map(all_values, &Map.get(&1, :plausibility, 0.5))
      
      # Simplified combination
      result = Enum.reduce(plausibility_values, 1, fn pl, acc -> acc * pl end)
      
      # Ensure result is within bounds
      min(result, 1.0)
    end

    @doc """
    Compares two uncertain beliefs and determines if they agree
    """
    @spec beliefs_agree?(BeliefAtom.t(), BeliefAtom.t(), float()) :: boolean()
    def beliefs_agree?(belief1, belief2, threshold \\ 0.7) do
      # Get uncertainty types and values
      type1 = get_in(belief1.metadata, [:uncertainty_type])
      values1 = get_in(belief1.metadata, [:uncertainty_values]) || %{}
      
      type2 = get_in(belief2.metadata, [:uncertainty_type])
      values2 = get_in(belief2.metadata, [:uncertainty_values]) || %{}
      
      # If either doesn't have uncertainty representation, use simple content comparison
      if is_nil(type1) or is_nil(type2) do
        belief1.content == belief2.content
      else
        # Check if content is the same
        if belief1.content != belief2.content do
          false
        else
          # Contents match, compute agreement based on uncertainty type
          case {type1, type2} do
            {:probabilistic, :probabilistic} ->
              # For probabilistic, compare probability values
              abs(Map.get(values1, :probability, 0.5) - Map.get(values2, :probability, 0.5)) < (1 - threshold)
              
            {:fuzzy, :fuzzy} ->
              # For fuzzy, compare membership values
              abs(Map.get(values1, :membership, 0.5) - Map.get(values2, :membership, 0.5)) < (1 - threshold)
              
            {:dempster_shafer, :dempster_shafer} ->
              # For DS, check if belief and plausibility intervals overlap significantly
              belief1_lower = Map.get(values1, :belief, 0.0)
              belief1_upper = Map.get(values1, :plausibility, 1.0)
              
              belief2_lower = Map.get(values2, :belief, 0.0)
              belief2_upper = Map.get(values2, :plausibility, 1.0)
              
              # Compute overlap
              overlap_lower = max(belief1_lower, belief2_lower)
              overlap_upper = min(belief1_upper, belief2_upper)
              
              if overlap_upper < overlap_lower do
                false  # No overlap
              else
                # Compute Jaccard similarity of intervals
                overlap_size = overlap_upper - overlap_lower
                union_size = max(belief1_upper, belief2_upper) - min(belief1_lower, belief2_lower)
                
                overlap_size / union_size >= threshold
              end
              
            _ ->
              # Different uncertainty types, fall back to confidence comparison
              abs(belief1.confidence - belief2.confidence) < (1 - threshold)
          end
        end
      end
    end
  end

  @doc """
  Creates a new belief atom
  """
  @spec create_belief(any(), atom() | pid(), float(), keyword()) :: BeliefAtom.t()
  def create_belief(content, source, confidence, options \\ []) do
    BeliefAtom.new(content, source, confidence, options)
  end

  @doc """
  Creates a new belief set for an agent
  """
  @spec create_belief_set(atom() | pid(), keyword()) :: BeliefSet.t()
  def create_belief_set(agent_id, options \\ []) do
    BeliefSet.new(agent_id, options)
  end

  @doc """
  Adds a belief to a belief set
  """
  @spec add_belief(BeliefSet.t(), BeliefAtom.t()) :: BeliefSet.t()
  def add_belief(belief_set, belief) do
    BeliefSet.add_belief(belief_set, belief)
  end

  @doc """
  Propagates a belief to multiple agents
  """
  @spec propagate_belief(BeliefAtom.t(), list(pid()), keyword()) :: map()
  def propagate_belief(belief, target_agents, options \\ []) do
    AsyncUpdates.propagate_belief(belief, target_agents, options)
  end

  @doc """
  Processes an incoming belief update
  """
  @spec process_belief_update(BeliefAtom.t(), BeliefSet.t(), keyword()) :: {BeliefSet.t(), :accepted | :rejected}
  def process_belief_update(belief, belief_set, options \\ []) do
    AsyncUpdates.process_belief_update(belief, belief_set, options)
  end

  @doc """
  Synchronizes two belief sets
  """
  @spec synchronize_beliefs(BeliefSet.t(), BeliefSet.t(), keyword()) :: {BeliefSet.t(), BeliefSet.t()}
  def synchronize_beliefs(belief_set1, belief_set2, options \\ []) do
    AsyncUpdates.synchronize_beliefs(belief_set1, belief_set2, options)
  end

  @doc """
  Verifies if a set of belief sets have converged
  """
  @spec verify_convergence(list(BeliefSet.t()), keyword()) :: {boolean(), float()}
  def verify_convergence(belief_sets, options \\ []) do
    AsyncUpdates.verify_convergence(belief_sets, options)
  end

  @doc """
  Resolves a conflict between two beliefs
  """
  @spec resolve_conflict(BeliefAtom.t(), BeliefAtom.t(), atom()) :: BeliefAtom.t()
  def resolve_conflict(belief1, belief2, strategy \\ :probabilistic) do
    ConflictResolution.resolve_conflict(belief1, belief2, strategy)
  end

  @doc """
  Resolves all conflicts in a belief set
  """
  @spec resolve_all_conflicts(BeliefSet.t(), atom()) :: BeliefSet.t()
  def resolve_all_conflicts(belief_set, strategy \\ :probabilistic) do
    ConflictResolution.resolve_all_conflicts(belief_set, strategy)
  end

  @doc """
  Creates a belief with uncertainty representation
  """
  @spec create_uncertain_belief(any(), atom() | pid(), UncertaintyRepresentation.uncertainty_type(), map(), keyword()) :: BeliefAtom.t()
  def create_uncertain_belief(content, source, uncertainty_type, uncertainty_values, options \\ []) do
    UncertaintyRepresentation.create_uncertain_belief(
      content, 
      source, 
      uncertainty_type, 
      uncertainty_values, 
      options
    )
  end

  @doc """
  Aggregates multiple uncertain beliefs about the same content
  """
  @spec aggregate_beliefs(list(BeliefAtom.t()), atom()) :: BeliefAtom.t()
  def aggregate_beliefs(beliefs, method \\ :weighted_average) do
    UncertaintyRepresentation.aggregate_beliefs(beliefs, method)
  end

  @doc """
  Checks if two beliefs agree with each other
  """
  @spec beliefs_agree?(BeliefAtom.t(), BeliefAtom.t(), float()) :: boolean()
  def beliefs_agree?(belief1, belief2, threshold \\ 0.7) do
    UncertaintyRepresentation.beliefs_agree?(belief1, belief2, threshold)
  end
end