defmodule Automata.Reasoning.Cognitive.NeuralSymbolic.SemanticGrounding do
  @moduledoc """
  Semantic Grounding System for Neural-Symbolic Integration

  This module provides mechanisms for establishing stable connections between
  abstract symbols and their grounded meaning in the environment:
  - Cross-modal concept anchoring with environmental feedback
  - Symbol emergence mechanisms with stability measures
  - Grounding verification protocols
  """

  alias Automata.Reasoning.Cognitive.NeuralSymbolic.TranslationFramework
  alias Automata.Reasoning.Cognitive.ContextualReasoning.KnowledgeRepresentation.SemanticNetwork

  defmodule ConceptGrounding do
    @moduledoc """
    Manages the grounding of abstract concepts in sensory and perceptual data.
    """

    @type concept :: atom() | String.t()
    @type modality :: :visual | :auditory | :proprioceptive | :semantic | :abstract
    @type sensory_data :: %{
            modality: modality(),
            data: any(),
            metadata: map()
          }
    @type grounding_result :: %{
            concept: concept(),
            groundings: %{optional(modality()) => map()},
            confidence: float(),
            stability: float()
          }

    @doc """
    Grounds a concept across multiple modalities using available sensory data.
    """
    @spec ground_concept(concept(), list(sensory_data()), keyword()) :: grounding_result
    def ground_concept(concept, sensory_data, options \\ []) do
      # Process each modality
      groundings =
        sensory_data
        |> Enum.group_by(& &1.modality)
        |> Enum.map(fn {modality, data_list} ->
          {modality, ground_in_modality(concept, data_list, modality, options)}
        end)
        |> Enum.into(%{})

      # Calculate overall grounding confidence and stability
      confidence = calculate_grounding_confidence(concept, groundings)
      stability = calculate_grounding_stability(concept, groundings)

      %{
        concept: concept,
        groundings: groundings,
        confidence: confidence,
        stability: stability
      }
    end

    @doc """
    Grounds a concept in a specific modality.
    """
    @spec ground_in_modality(concept(), list(sensory_data()), modality(), keyword()) :: map()
    defp ground_in_modality(concept, data_list, modality, options) do
      # Select appropriate grounding method based on modality
      grounding_fn = get_grounding_function(modality)

      # Apply the grounding function to the sensory data
      grounding_data = grounding_fn.(concept, data_list, options)

      # Return the grounding data with metadata
      %{
        representation: grounding_data.representation,
        confidence: grounding_data.confidence,
        features: grounding_data.features,
        last_updated: DateTime.utc_now()
      }
    end

    @doc """
    Selects the appropriate grounding function based on modality.
    """
    @spec get_grounding_function(modality()) :: function()
    defp get_grounding_function(:visual) do
      # Function for grounding in visual modality
      fn concept, data_list, options ->
        # Extract visual features relevant to the concept
        # In a real implementation, this would use computer vision techniques
        %{
          representation: %{prototype: [0.1, 0.2, 0.3]},
          confidence: 0.85,
          features: [:color, :shape, :texture]
        }
      end
    end

    defp get_grounding_function(:auditory) do
      # Function for grounding in auditory modality
      fn concept, data_list, options ->
        # Extract auditory features relevant to the concept
        # In a real implementation, this would use audio processing techniques
        %{
          representation: %{spectral_signature: [0.5, 0.6, 0.7]},
          confidence: 0.75,
          features: [:frequency, :amplitude, :duration]
        }
      end
    end

    defp get_grounding_function(:proprioceptive) do
      # Function for grounding in proprioceptive modality
      fn concept, data_list, options ->
        # Extract proprioceptive features relevant to the concept
        # In a real implementation, this would use movement/position data
        %{
          representation: %{movement_pattern: [0.2, 0.3, 0.4]},
          confidence: 0.8,
          features: [:position, :force, :velocity]
        }
      end
    end

    defp get_grounding_function(:semantic) do
      # Function for grounding in semantic modality
      fn concept, data_list, options ->
        # Extract semantic features relevant to the concept
        # In a real implementation, this would use linguistic/semantic data
        %{
          representation: %{semantic_vector: [0.4, 0.5, 0.6]},
          confidence: 0.9,
          features: [:context, :relations, :attributes]
        }
      end
    end

    defp get_grounding_function(:abstract) do
      # Function for grounding in abstract modality
      fn concept, data_list, options ->
        # Create abstract representation for the concept
        # In a real implementation, this would use formal/logical structures
        %{
          representation: %{formal_definition: [concept: concept, properties: []]},
          confidence: 0.7,
          features: [:logic, :mathematics, :categories]
        }
      end
    end

    @doc """
    Calculates the overall confidence in the concept grounding.
    """
    @spec calculate_grounding_confidence(concept(), map()) :: float()
    defp calculate_grounding_confidence(concept, groundings) do
      # If no groundings, zero confidence
      if map_size(groundings) == 0 do
        0.0
      else
        # Calculate weighted average of confidences across modalities
        # with weights based on modality reliability for this concept type
        weights = %{
          visual: 0.3,
          auditory: 0.2,
          proprioceptive: 0.15,
          semantic: 0.25,
          abstract: 0.1
        }

        total_weight = 0
        weighted_sum = 0

        {weighted_sum, total_weight} =
          Enum.reduce(groundings, {0, 0}, fn {modality, data}, {sum, weight} ->
            modality_weight = Map.get(weights, modality, 0.1)
            {sum + data.confidence * modality_weight, weight + modality_weight}
          end)

        # Return normalized confidence
        if total_weight > 0, do: weighted_sum / total_weight, else: 0.0
      end
    end

    @doc """
    Calculates the stability of the concept grounding.
    """
    @spec calculate_grounding_stability(concept(), map()) :: float()
    defp calculate_grounding_stability(concept, groundings) do
      # In a real implementation, this would:
      # - Consider temporal consistency of groundings over time
      # - Measure coherence across modalities
      # - Evaluate resistance to perturbations

      # For now, use a simple heuristic based on number of modalities and confidence
      modality_count = map_size(groundings)
      if modality_count == 0 do
        0.0
      else
        # Calculate stability as function of modality coverage and confidence
        confidences = Enum.map(groundings, fn {_, data} -> data.confidence end)
        avg_confidence = Enum.sum(confidences) / length(confidences)
        
        # Higher stability with more modalities and higher confidence
        modality_factor = :math.tanh(modality_count / 3)  # Saturates as modalities increase
        stability = modality_factor * avg_confidence
        
        stability
      end
    end
  end

  defmodule SymbolEmergence do
    @moduledoc """
    Manages the emergence of symbols from patterns in sensory and perceptual data.
    """

    @type pattern :: map()
    @type symbol :: atom() | String.t()
    @type emergence_result :: %{
            symbol: symbol(),
            pattern: pattern(),
            confidence: float(),
            stability: float()
          }

    @doc """
    Identifies emergent symbols from observed patterns.
    """
    @spec identify_symbols(list(pattern()), keyword()) :: list(emergence_result)
    def identify_symbols(patterns, options \\ []) do
      # Apply pattern clustering to identify distinct symbol candidates
      clusters = cluster_patterns(patterns, options)
      
      # Generate symbols for each cluster
      Enum.map(clusters, fn cluster ->
        symbol = generate_symbol_for_cluster(cluster, options)
        confidence = calculate_symbol_confidence(cluster, symbol)
        stability = calculate_symbol_stability(cluster, symbol)
        
        %{
          symbol: symbol,
          pattern: extract_prototype(cluster),
          confidence: confidence,
          stability: stability
        }
      end)
    end

    @doc """
    Clusters patterns to identify distinct categories.
    """
    @spec cluster_patterns(list(pattern()), keyword()) :: list(list(pattern()))
    defp cluster_patterns(patterns, options) do
      # In a real implementation, this would use clustering algorithms
      # like k-means, hierarchical clustering, or density-based methods
      
      # For now, return a simple placeholder clustering
      [patterns]
    end

    @doc """
    Generates a symbolic representation for a pattern cluster.
    """
    @spec generate_symbol_for_cluster(list(pattern()), keyword()) :: symbol()
    defp generate_symbol_for_cluster(cluster, options) do
      # In a real implementation, this would generate a meaningful symbol
      # based on cluster characteristics and existing symbol vocabulary
      
      # For now, generate a placeholder symbol
      :"concept_#{:rand.uniform(1000)}"
    end

    @doc """
    Extracts a prototype pattern from a cluster of patterns.
    """
    @spec extract_prototype(list(pattern())) :: pattern()
    defp extract_prototype(cluster) do
      # In a real implementation, this would compute a representative
      # prototype or archetype for the cluster
      
      # For now, just use the first pattern as prototype
      List.first(cluster)
    end

    @doc """
    Calculates confidence in the symbol as representative of the pattern cluster.
    """
    @spec calculate_symbol_confidence(list(pattern()), symbol()) :: float()
    defp calculate_symbol_confidence(cluster, _symbol) do
      # In a real implementation, this would evaluate how well the symbol
      # captures the essence of the pattern cluster
      
      # For now, use cluster cohesion as a proxy for confidence
      if length(cluster) <= 1 do
        0.9  # High confidence for singleton clusters
      else
        # Placeholder for cluster cohesion calculation
        0.8
      end
    end

    @doc """
    Calculates the stability of the symbol over time and variations.
    """
    @spec calculate_symbol_stability(list(pattern()), symbol()) :: float()
    defp calculate_symbol_stability(cluster, _symbol) do
      # In a real implementation, this would evaluate:
      # - Temporal consistency of the symbol
      # - Resistance to pattern variations
      # - Distinctiveness from other symbols
      
      # For now, use a simple heuristic based on cluster size and spread
      if length(cluster) <= 1 do
        0.7  # Moderate stability for singleton clusters
      else
        # Placeholder for stability calculation
        0.8
      end
    end

    @doc """
    Tracks the evolution of symbols over time and updates their representations.
    """
    @spec track_symbol_evolution(list(emergence_result()), list(pattern()), keyword()) :: list(emergence_result)
    def track_symbol_evolution(previous_symbols, new_patterns, options \\ []) do
      # Identify symbols from new patterns
      new_symbol_candidates = identify_symbols(new_patterns, options)
      
      # Match new candidates with existing symbols
      matched_symbols = match_with_existing_symbols(previous_symbols, new_symbol_candidates)
      
      # Update existing symbols and add new ones
      updated_symbols = update_symbols(previous_symbols, matched_symbols, options)
      
      updated_symbols
    end

    @doc """
    Matches new symbol candidates with existing symbols.
    """
    @spec match_with_existing_symbols(list(emergence_result()), list(emergence_result())) :: list({emergence_result(), emergence_result()})
    defp match_with_existing_symbols(existing_symbols, new_candidates) do
      # For each new candidate, find the best matching existing symbol
      Enum.flat_map(new_candidates, fn new_candidate ->
        # Calculate similarity with all existing symbols
        matches = Enum.map(existing_symbols, fn existing ->
          similarity = calculate_pattern_similarity(existing.pattern, new_candidate.pattern)
          {existing, new_candidate, similarity}
        end)
        
        # Select the best match if it exceeds threshold
        best_match = Enum.max_by(matches, fn {_, _, similarity} -> similarity end, fn -> nil end)
        
        case best_match do
          {existing, new, similarity} when similarity > 0.7 ->
            [{existing, new}]
          _ ->
            []  # No good match found
        end
      end)
    end

    @doc """
    Calculates similarity between two patterns.
    """
    @spec calculate_pattern_similarity(pattern(), pattern()) :: float()
    defp calculate_pattern_similarity(_pattern1, _pattern2) do
      # In a real implementation, this would compute a domain-appropriate
      # similarity metric between the patterns
      
      # For now, return a placeholder similarity
      0.8
    end

    @doc """
    Updates existing symbols with new information and adds new symbols.
    """
    @spec update_symbols(list(emergence_result()), list({emergence_result(), emergence_result()}), keyword()) :: list(emergence_result)
    defp update_symbols(existing_symbols, matched_symbols, options) do
      # Create a set of existing symbols that were matched
      matched_existing_ids = MapSet.new(matched_symbols, fn {existing, _} -> existing.symbol end)
      
      # Update matched symbols
      updated_matched = Enum.map(matched_symbols, fn {existing, new} ->
        update_symbol(existing, new, options)
      end)
      
      # Keep unmatched existing symbols
      kept_existing = Enum.filter(existing_symbols, fn symbol ->
        not MapSet.member?(matched_existing_ids, symbol.symbol)
      end)
      
      # Find new symbols that didn't match any existing ones
      matched_new_ids = MapSet.new(matched_symbols, fn {_, new} -> new.symbol end)
      all_new_ids = MapSet.new(Enum.map(matched_symbols, fn {_, new} -> new.symbol end))
      
      new_symbols = matched_symbols
      |> Enum.filter(fn {_, new} -> not MapSet.member?(matched_new_ids, new.symbol) end)
      |> Enum.map(fn {_, new} -> new end)
      
      # Combine updated, kept, and new symbols
      updated_matched ++ kept_existing ++ new_symbols
    end

    @doc """
    Updates a symbol with new information.
    """
    @spec update_symbol(emergence_result(), emergence_result(), keyword()) :: emergence_result()
    defp update_symbol(existing, new, options) do
      # Calculate update rate based on stability - stable symbols change more slowly
      update_rate = Keyword.get(options, :update_rate, 0.2) * (1 - existing.stability)
      
      # Update pattern (weighted average of existing and new)
      updated_pattern = merge_patterns(existing.pattern, new.pattern, update_rate)
      
      # Recalculate confidence and stability
      updated_confidence = existing.confidence * (1 - update_rate) + new.confidence * update_rate
      
      # Stability increases with consistent updates, decreases with divergent ones
      pattern_similarity = calculate_pattern_similarity(existing.pattern, new.pattern)
      stability_change = if pattern_similarity > 0.8, do: 0.05, else: -0.1
      updated_stability = min(1.0, max(0.0, existing.stability + stability_change))
      
      # Return updated symbol
      %{
        symbol: existing.symbol,
        pattern: updated_pattern,
        confidence: updated_confidence,
        stability: updated_stability
      }
    end

    @doc """
    Merges two patterns with specified weighting.
    """
    @spec merge_patterns(pattern(), pattern(), float()) :: pattern()
    defp merge_patterns(pattern1, pattern2, weight2) do
      # In a real implementation, this would perform a weighted merge
      # of the patterns based on their specific structure
      
      # For now, return a simple placeholder merge
      pattern1
    end
  end

  defmodule GroundingVerification do
    @moduledoc """
    Provides methods for verifying the quality and robustness of groundings.
    """

    @type verification_result :: %{
            valid: boolean(),
            score: float(),
            issues: list(map()),
            confidence: float()
          }

    @doc """
    Verifies the quality and robustness of concept groundings.
    """
    @spec verify_grounding(ConceptGrounding.grounding_result(), keyword()) :: verification_result
    def verify_grounding(grounding, options \\ []) do
      # Apply various verification checks
      checks =
        [
          check_cross_modal_consistency(grounding),
          check_temporal_stability(grounding),
          check_environmental_validity(grounding, options[:environment])
        ]

      # Aggregate results from all checks
      issues = Enum.flat_map(checks, & &1.issues)
      valid = Enum.all?(checks, & &1.valid)
      
      # Calculate overall grounding quality score
      weights = [0.4, 0.3, 0.3]  # Weights for different check types
      scores = Enum.map(checks, & &1.score)
      overall_score = weighted_average(scores, weights)
      
      # Calculate confidence in verification
      confidence = calculate_verification_confidence(grounding, checks)

      %{
        valid: valid,
        score: overall_score,
        issues: issues,
        confidence: confidence
      }
    end

    @doc """
    Checks consistency of groundings across modalities.
    """
    @spec check_cross_modal_consistency(ConceptGrounding.grounding_result()) :: %{
            valid: boolean(),
            score: float(),
            issues: list(map())
          }
    defp check_cross_modal_consistency(grounding) do
      # Get all available modalities
      modalities = Map.keys(grounding.groundings)
      
      # If fewer than 2 modalities, consistency check is not applicable
      if length(modalities) < 2 do
        %{valid: true, score: 1.0, issues: []}
      else
        # Check consistency between each pair of modalities
        consistency_checks = for m1 <- modalities, m2 <- modalities, m1 < m2 do
          check_consistency_between_modalities(
            grounding.concept,
            grounding.groundings[m1],
            m1,
            grounding.groundings[m2],
            m2
          )
        end
        
        # Aggregate all consistency checks
        issues = Enum.flat_map(consistency_checks, & &1.issues)
        valid = Enum.all?(consistency_checks, & &1.valid)
        avg_score = Enum.sum(Enum.map(consistency_checks, & &1.score)) / length(consistency_checks)
        
        %{
          valid: valid,
          score: avg_score,
          issues: issues
        }
      end
    end

    @doc """
    Checks consistency between two specific modalities.
    """
    @spec check_consistency_between_modalities(any(), map(), atom(), map(), atom()) :: %{
            valid: boolean(),
            score: float(),
            issues: list(map())
          }
    defp check_consistency_between_modalities(concept, grounding1, modality1, grounding2, modality2) do
      # In a real implementation, this would calculate appropriate
      # cross-modal consistency metrics based on the specific modalities
      
      # For now, return a placeholder consistency check
      %{
        valid: true,
        score: 0.85,
        issues: []
      }
    end

    @doc """
    Checks temporal stability of groundings over time.
    """
    @spec check_temporal_stability(ConceptGrounding.grounding_result()) :: %{
            valid: boolean(),
            score: float(),
            issues: list(map())
          }
    defp check_temporal_stability(grounding) do
      # In a real implementation, this would:
      # - Analyze history of groundings over time
      # - Evaluate consistency and drift rates
      # - Identify problematic fluctuations
      
      # For now, use the stability score directly
      %{
        valid: grounding.stability > 0.6,
        score: grounding.stability,
        issues: if(grounding.stability < 0.6, do: [%{type: :low_stability, severity: :medium}], else: [])
      }
    end

    @doc """
    Checks validity of groundings in the current environment.
    """
    @spec check_environmental_validity(ConceptGrounding.grounding_result(), map() | nil) :: %{
            valid: boolean(),
            score: float(),
            issues: list(map())
          }
    defp check_environmental_validity(_grounding, nil) do
      # If no environment provided, skip this check
      %{valid: true, score: 1.0, issues: []}
    end

    defp check_environmental_validity(grounding, environment) do
      # In a real implementation, this would:
      # - Verify the groundings against current environmental state
      # - Check for contradictions with observed reality
      # - Evaluate practical usability of the groundings
      
      # For now, return a placeholder validity check
      %{
        valid: true,
        score: 0.9,
        issues: []
      }
    end

    @doc """
    Calculates confidence in verification results.
    """
    @spec calculate_verification_confidence(ConceptGrounding.grounding_result(), list(map())) :: float()
    defp calculate_verification_confidence(grounding, checks) do
      # In a real implementation, this would consider:
      # - Comprehensiveness of the verification
      # - Quality of the available data
      # - Known limitations of verification methods
      0.9
    end

    @doc """
    Verifies a symbol emergence result.
    """
    @spec verify_symbol_emergence(SymbolEmergence.emergence_result(), keyword()) :: verification_result
    def verify_symbol_emergence(emergence_result, options \\ []) do
      # Apply various verification checks
      checks =
        [
          check_symbol_distinctiveness(emergence_result, options[:existing_symbols]),
          check_symbol_meaningfulness(emergence_result),
          check_symbol_usefulness(emergence_result, options[:use_cases])
        ]

      # Aggregate results from all checks
      issues = Enum.flat_map(checks, & &1.issues)
      valid = Enum.all?(checks, & &1.valid)
      
      # Calculate overall emergence quality score
      weights = [0.4, 0.4, 0.2]  # Weights for different check types
      scores = Enum.map(checks, & &1.score)
      overall_score = weighted_average(scores, weights)
      
      # Calculate confidence in verification
      confidence = calculate_emergence_verification_confidence(emergence_result, checks)

      %{
        valid: valid,
        score: overall_score,
        issues: issues,
        confidence: confidence
      }
    end

    @doc """
    Checks distinctiveness of a symbol compared to existing symbols.
    """
    @spec check_symbol_distinctiveness(SymbolEmergence.emergence_result(), list() | nil) :: %{
            valid: boolean(),
            score: float(),
            issues: list(map())
          }
    defp check_symbol_distinctiveness(_emergence_result, nil) do
      # If no existing symbols provided, assume distinctiveness
      %{valid: true, score: 1.0, issues: []}
    end

    defp check_symbol_distinctiveness(emergence_result, existing_symbols) do
      # In a real implementation, this would:
      # - Calculate similarity to existing symbols
      # - Evaluate potential confusion or overlap
      # - Check for redundancy
      
      # For now, return a placeholder distinctiveness check
      %{
        valid: true,
        score: 0.9,
        issues: []
      }
    end

    @doc """
    Checks meaningfulness of an emergent symbol.
    """
    @spec check_symbol_meaningfulness(SymbolEmergence.emergence_result()) :: %{
            valid: boolean(),
            score: float(),
            issues: list(map())
          }
    defp check_symbol_meaningfulness(emergence_result) do
      # In a real implementation, this would:
      # - Evaluate if the symbol captures a coherent concept
      # - Check if the pattern is robust and consistent
      # - Verify the symbol has semantic interpretability
      
      # For now, use the confidence score directly
      %{
        valid: emergence_result.confidence > 0.7,
        score: emergence_result.confidence,
        issues: if(emergence_result.confidence < 0.7, do: [%{type: :low_confidence, severity: :medium}], else: [])
      }
    end

    @doc """
    Checks usefulness of an emergent symbol for specified use cases.
    """
    @spec check_symbol_usefulness(SymbolEmergence.emergence_result(), list() | nil) :: %{
            valid: boolean(),
            score: float(),
            issues: list(map())
          }
    defp check_symbol_usefulness(_emergence_result, nil) do
      # If no use cases provided, assume neutral usefulness
      %{valid: true, score: 0.5, issues: []}
    end

    defp check_symbol_usefulness(emergence_result, use_cases) do
      # In a real implementation, this would:
      # - Evaluate relevance to specified use cases
      # - Check practical utility in reasoning or decision-making
      # - Assess impact on system capabilities
      
      # For now, return a placeholder usefulness check
      %{
        valid: true,
        score: 0.8,
        issues: []
      }
    end

    @doc """
    Calculates confidence in symbol emergence verification.
    """
    @spec calculate_emergence_verification_confidence(SymbolEmergence.emergence_result(), list(map())) :: float()
    defp calculate_emergence_verification_confidence(emergence_result, checks) do
      # In a real implementation, this would consider:
      # - Comprehensiveness of the verification
      # - Quality of the available data
      # - Known limitations of verification methods
      0.85
    end
  end

  @doc """
  Calculates weighted average of values.
  """
  @spec weighted_average(list(number()), list(number())) :: float()
  def weighted_average(values, weights) do
    Enum.zip(values, weights)
    |> Enum.map(fn {v, w} -> v * w end)
    |> Enum.sum()
  end

  @doc """
  Grounds a concept across multiple modalities.
  """
  @spec ground_concept(any(), list(map()), keyword()) :: map()
  def ground_concept(concept, sensory_data, options \\ []) do
    ConceptGrounding.ground_concept(concept, sensory_data, options)
  end

  @doc """
  Identifies emergent symbols from patterns.
  """
  @spec identify_symbols(list(map()), keyword()) :: list(map())
  def identify_symbols(patterns, options \\ []) do
    SymbolEmergence.identify_symbols(patterns, options)
  end

  @doc """
  Tracks evolution of symbols over time.
  """
  @spec track_symbol_evolution(list(map()), list(map()), keyword()) :: list(map())
  def track_symbol_evolution(previous_symbols, new_patterns, options \\ []) do
    SymbolEmergence.track_symbol_evolution(previous_symbols, new_patterns, options)
  end

  @doc """
  Verifies the quality of concept groundings.
  """
  @spec verify_grounding(map(), keyword()) :: map()
  def verify_grounding(grounding, options \\ []) do
    GroundingVerification.verify_grounding(grounding, options)
  end

  @doc """
  Verifies the quality of symbol emergence.
  """
  @spec verify_symbol_emergence(map(), keyword()) :: map()
  def verify_symbol_emergence(emergence_result, options \\ []) do
    GroundingVerification.verify_symbol_emergence(emergence_result, options)
  end

  @doc """
  Provides a unified interface to the neural-symbolic integration system.
  """
  @spec integrate_concept(any(), list(map()), keyword()) :: map()
  def integrate_concept(concept, sensory_data, options \\ []) do
    # Ground the concept across modalities
    grounding = ground_concept(concept, sensory_data, options)
    
    # Verify the grounding quality
    verification = verify_grounding(grounding, options)
    
    # Create symbolic and neural representations
    symbolic_representation = create_symbolic_representation(concept, grounding)
    neural_representation = create_neural_representation(concept, grounding)
    
    # Return the integrated result
    %{
      concept: concept,
      grounding: grounding,
      verification: verification,
      symbolic: symbolic_representation,
      neural: neural_representation,
      valid: verification.valid,
      confidence: grounding.confidence * verification.confidence
    }
  end

  @doc """
  Creates a symbolic representation from grounding.
  """
  @spec create_symbolic_representation(any(), map()) :: map()
  defp create_symbolic_representation(concept, grounding) do
    # In a real implementation, this would extract and organize
    # symbolic knowledge from the grounded concept
    
    # For now, return a placeholder symbolic representation
    %{
      type: :concept,
      id: concept,
      attributes: [],
      relations: []
    }
  end

  @doc """
  Creates a neural representation from grounding.
  """
  @spec create_neural_representation(any(), map()) :: map()
  defp create_neural_representation(concept, grounding) do
    # In a real implementation, this would generate a unified
    # neural representation from the multimodal groundings
    
    # For now, return a placeholder neural representation
    %{
      vector: [0.1, 0.2, 0.3, 0.4],
      metadata: %{
        source: :grounding,
        modalities: Map.keys(grounding.groundings)
      }
    }
  end
end