defmodule Object.NeuroSymbolicReasoning do
  @moduledoc """
  Advanced neuro-symbolic reasoning engine for AAOS objects.
  
  Combines deep neural networks with symbolic reasoning for sophisticated
  cognitive capabilities including:
  
  - Multi-modal transformer architectures
  - Graph neural networks for relational reasoning
  - Differentiable neural symbolic programming
  - Attention-based memory architectures
  - Causal inference and counterfactual reasoning
  - Meta-cognitive reflection and self-awareness
  - Hierarchical reasoning with abstraction levels
  - Uncertainty quantification and epistemic reasoning
  """
  
  use GenServer
  require Logger
  
  # Neural Architecture Constants
  @transformer_dims 1024
  @attention_heads 16
  @num_layers 12
  @vocab_size 50000
  @max_sequence_length 2048
  @hidden_dims 4096
  
  # Symbolic Reasoning Constants
  @max_proof_depth 20
  @inference_steps 1000
  @knowledge_base_size 10000
  @rule_complexity_limit 10
  
  @type tensor :: %{
    data: [float()],
    shape: [non_neg_integer()],
    dtype: :float32 | :float64 | :int32 | :int64
  }
  
  @type attention_weights :: %{
    query: tensor(),
    key: tensor(),
    value: tensor(),
    output: tensor()
  }
  
  @type symbolic_expression :: %{
    type: :atom | :variable | :compound | :quantified,
    functor: binary() | nil,
    args: [symbolic_expression()],
    variables: [binary()],
    constraints: [symbolic_expression()]
  }
  
  @type proof_step :: %{
    rule: binary(),
    premises: [symbolic_expression()],
    conclusion: symbolic_expression(),
    justification: binary(),
    confidence: float()
  }
  
  @type reasoning_trace :: %{
    input: term(),
    neural_activations: %{non_neg_integer() => tensor()},
    symbolic_derivations: [proof_step()],
    attention_patterns: %{non_neg_integer() => attention_weights()},
    final_conclusion: term(),
    confidence: float(),
    explanation: binary()
  }
  
  @type cognitive_state :: %{
    working_memory: [symbolic_expression()],
    episodic_memory: %{term() => reasoning_trace()},
    semantic_knowledge: %{binary() => symbolic_expression()},
    neural_parameters: %{binary() => tensor()},
    meta_cognition: %{
      self_model: symbolic_expression(),
      uncertainty_estimates: %{term() => float()},
      reasoning_strategies: [binary()]
    }
  }
  
  @type state :: %{
    cognitive_state: cognitive_state(),
    neural_networks: %{
      transformer: map(),
      graph_net: map(),
      meta_net: map()
    },
    symbolic_systems: %{
      knowledge_base: map(),
      inference_engine: map(),
      proof_search: map()
    },
    integration_layer: map(),
    performance_metrics: map()
  }
  
  # Client API
  
  @doc """
  Starts the neuro-symbolic reasoning engine.
  """
  def start_link(opts \\ []) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  @doc """
  Performs multi-modal reasoning on complex input.
  """
  @spec reason(term(), map()) :: {:ok, reasoning_trace()} | {:error, term()}
  def reason(input, context \\ %{}) do
    GenServer.call(__MODULE__, {:reason, input, context}, 30000)
  end
  
  @doc """
  Learns from experience and updates neural and symbolic components.
  """
  @spec learn_from_experience(reasoning_trace(), term()) :: :ok | {:error, term()}
  def learn_from_experience(trace, feedback) do
    GenServer.call(__MODULE__, {:learn, trace, feedback})
  end
  
  @doc """
  Performs causal inference and counterfactual reasoning.
  """
  @spec causal_inference(symbolic_expression(), [symbolic_expression()]) :: 
    {:ok, [symbolic_expression()]} | {:error, term()}
  def causal_inference(query, evidence) do
    GenServer.call(__MODULE__, {:causal_inference, query, evidence})
  end
  
  @doc """
  Generates explanations for reasoning decisions.
  """
  @spec explain_reasoning(reasoning_trace()) :: {:ok, binary()} | {:error, term()}
  def explain_reasoning(trace) do
    GenServer.call(__MODULE__, {:explain, trace})
  end
  
  @doc """
  Meta-cognitive self-reflection and strategy adaptation.
  """
  @spec meta_reflect() :: {:ok, map()} | {:error, term()}
  def meta_reflect do
    GenServer.call(__MODULE__, :meta_reflect)
  end
  
  @doc """
  Performs few-shot learning with minimal examples.
  """
  @spec few_shot_learn([{term(), term()}], term()) :: {:ok, term()} | {:error, term()}
  def few_shot_learn(examples, query) do
    GenServer.call(__MODULE__, {:few_shot_learn, examples, query})
  end
  
  # Server Callbacks
  
  @impl true
  def init(opts) do
    # Initialize neural networks
    neural_networks = %{
      transformer: initialize_transformer(),
      graph_net: initialize_graph_network(),
      meta_net: initialize_meta_network()
    }
    
    # Initialize symbolic systems
    symbolic_systems = %{
      knowledge_base: initialize_knowledge_base(),
      inference_engine: initialize_inference_engine(),
      proof_search: initialize_proof_search()
    }
    
    # Initialize cognitive state
    cognitive_state = %{
      working_memory: [],
      episodic_memory: %{},
      semantic_knowledge: initialize_semantic_knowledge(),
      neural_parameters: extract_neural_parameters(neural_networks),
      meta_cognition: initialize_meta_cognition()
    }
    
    state = %{
      cognitive_state: cognitive_state,
      neural_networks: neural_networks,
      symbolic_systems: symbolic_systems,
      integration_layer: initialize_integration_layer(),
      performance_metrics: %{
        reasoning_accuracy: 0.0,
        inference_speed: 0.0,
        explanation_quality: 0.0
      }
    }
    
    Logger.info("Neuro-symbolic reasoning engine initialized with #{@transformer_dims}D transformer")
    {:ok, state}
  end
  
  @impl true
  def handle_call({:reason, input, context}, _from, state) do
    start_time = System.monotonic_time(:microsecond)
    
    case perform_reasoning(input, context, state) do
      {:ok, trace} ->
        # Update performance metrics
        end_time = System.monotonic_time(:microsecond)
        inference_time = (end_time - start_time) / 1_000_000
        
        new_metrics = %{state.performance_metrics | 
          inference_speed: update_moving_average(
            state.performance_metrics.inference_speed, 
            inference_time
          )
        }
        
        # Update cognitive state with new experience
        new_cognitive_state = update_cognitive_state(state.cognitive_state, trace)
        
        new_state = %{state | 
          performance_metrics: new_metrics,
          cognitive_state: new_cognitive_state
        }
        
        {:reply, {:ok, trace}, new_state}
        
      error ->
        {:reply, error, state}
    end
  end
  
  @impl true
  def handle_call({:learn, trace, feedback}, _from, state) do
    case update_from_feedback(trace, feedback, state) do
      {:ok, new_state} ->
        {:reply, :ok, new_state}
      error ->
        {:reply, error, state}
    end
  end
  
  @impl true
  def handle_call({:causal_inference, query, evidence}, _from, state) do
    case perform_causal_inference(query, evidence, state) do
      {:ok, conclusions} ->
        {:reply, {:ok, conclusions}, state}
      error ->
        {:reply, error, state}
    end
  end
  
  @impl true
  def handle_call({:explain, trace}, _from, state) do
    explanation = generate_explanation(trace, state)
    {:reply, {:ok, explanation}, state}
  end
  
  @impl true
  def handle_call(:meta_reflect, _from, state) do
    case perform_meta_reflection(state) do
      {:ok, insights, new_state} ->
        {:reply, {:ok, insights}, new_state}
      error ->
        {:reply, error, state}
    end
  end
  
  @impl true
  def handle_call({:few_shot_learn, examples, query}, _from, state) do
    case perform_few_shot_learning(examples, query, state) do
      {:ok, result} ->
        {:reply, {:ok, result}, state}
      error ->
        {:reply, error, state}
    end
  end
  
  # Neural Network Initialization
  
  defp initialize_transformer do
    %{
      embedding_layer: initialize_embeddings(@vocab_size, @transformer_dims),
      positional_encoding: generate_positional_encoding(@max_sequence_length, @transformer_dims),
      encoder_layers: initialize_transformer_layers(@num_layers, @transformer_dims, @attention_heads),
      output_projection: initialize_linear_layer(@transformer_dims, @vocab_size),
      layer_norm: initialize_layer_norm(@transformer_dims)
    }
  end
  
  defp initialize_graph_network do
    %{
      node_encoder: initialize_linear_layer(128, @transformer_dims),
      edge_encoder: initialize_linear_layer(64, @transformer_dims),
      message_passing_layers: initialize_gnn_layers(6, @transformer_dims),
      global_pooling: initialize_set2set_pooling(@transformer_dims),
      output_decoder: initialize_linear_layer(@transformer_dims, 512)
    }
  end
  
  defp initialize_meta_network do
    %{
      strategy_selector: initialize_linear_layer(@transformer_dims, 10),
      uncertainty_estimator: initialize_bayesian_layer(@transformer_dims, 1),
      confidence_predictor: initialize_linear_layer(@transformer_dims, 1),
      self_model_encoder: initialize_transformer_encoder(6, @transformer_dims, 8)
    }
  end
  
  defp initialize_embeddings(vocab_size, dims) do
    # Random initialization following Xavier/Glorot scheme
    scale = :math.sqrt(2.0 / (vocab_size + dims))
    data = for _ <- 1..(vocab_size * dims) do
      (:rand.uniform() - 0.5) * 2 * scale
    end
    
    %{
      data: data,
      shape: [vocab_size, dims],
      dtype: :float32
    }
  end
  
  defp generate_positional_encoding(max_len, dims) do
    positions = for pos <- 0..(max_len - 1) do
      for i <- 0..(dims - 1) do
        if rem(i, 2) == 0 do
          :math.sin(pos / :math.pow(10000, i / dims))
        else
          :math.cos(pos / :math.pow(10000, (i - 1) / dims))
        end
      end
    end |> List.flatten()
    
    %{
      data: positions,
      shape: [max_len, dims],
      dtype: :float32
    }
  end
  
  defp initialize_transformer_layers(num_layers, dims, heads) do
    for layer <- 1..num_layers do
      %{
        "layer_#{layer}" => %{
          multi_head_attention: initialize_multi_head_attention(dims, heads),
          feed_forward: initialize_feed_forward(dims, @hidden_dims),
          layer_norm_1: initialize_layer_norm(dims),
          layer_norm_2: initialize_layer_norm(dims),
          dropout: 0.1
        }
      }
    end |> Enum.reduce(%{}, &Map.merge/2)
  end
  
  defp initialize_multi_head_attention(dims, heads) do
    head_dim = div(dims, heads)
    
    %{
      query_projection: initialize_linear_layer(dims, dims),
      key_projection: initialize_linear_layer(dims, dims),
      value_projection: initialize_linear_layer(dims, dims),
      output_projection: initialize_linear_layer(dims, dims),
      num_heads: heads,
      head_dim: head_dim,
      scale: 1.0 / :math.sqrt(head_dim)
    }
  end
  
  defp initialize_feed_forward(input_dims, hidden_dims) do
    %{
      linear_1: initialize_linear_layer(input_dims, hidden_dims),
      linear_2: initialize_linear_layer(hidden_dims, input_dims),
      activation: :gelu,
      dropout: 0.1
    }
  end
  
  defp initialize_linear_layer(input_size, output_size) do
    scale = :math.sqrt(2.0 / (input_size + output_size))
    weight_data = for _ <- 1..(input_size * output_size) do
      (:rand.uniform() - 0.5) * 2 * scale
    end
    bias_data = for _ <- 1..output_size, do: 0.0
    
    %{
      weight: %{data: weight_data, shape: [output_size, input_size], dtype: :float32},
      bias: %{data: bias_data, shape: [output_size], dtype: :float32}
    }
  end
  
  defp initialize_layer_norm(dims) do
    %{
      gamma: %{data: List.duplicate(1.0, dims), shape: [dims], dtype: :float32},
      beta: %{data: List.duplicate(0.0, dims), shape: [dims], dtype: :float32},
      epsilon: 1.0e-5
    }
  end
  
  defp initialize_gnn_layers(num_layers, dims) do
    for layer <- 1..num_layers do
      %{
        "gnn_layer_#{layer}" => %{
          message_function: initialize_linear_layer(dims * 2, dims),
          update_function: initialize_linear_layer(dims * 2, dims),
          aggregation: :sum,
          residual: true,
          layer_norm: initialize_layer_norm(dims)
        }
      }
    end |> Enum.reduce(%{}, &Map.merge/2)
  end
  
  defp initialize_set2set_pooling(dims) do
    %{
      lstm_cell: initialize_lstm_cell(dims, dims),
      attention: initialize_linear_layer(dims * 2, 1),
      num_steps: 3
    }
  end
  
  defp initialize_lstm_cell(input_size, hidden_size) do
    %{
      input_gate: initialize_linear_layer(input_size + hidden_size, hidden_size),
      forget_gate: initialize_linear_layer(input_size + hidden_size, hidden_size),
      output_gate: initialize_linear_layer(input_size + hidden_size, hidden_size),
      cell_gate: initialize_linear_layer(input_size + hidden_size, hidden_size)
    }
  end
  
  defp initialize_bayesian_layer(input_size, output_size) do
    %{
      weight_mean: initialize_linear_layer(input_size, output_size),
      weight_logvar: initialize_linear_layer(input_size, output_size),
      bias_mean: %{data: List.duplicate(0.0, output_size), shape: [output_size], dtype: :float32},
      bias_logvar: %{data: List.duplicate(-3.0, output_size), shape: [output_size], dtype: :float32},
      prior_mean: 0.0,
      prior_var: 1.0
    }
  end
  
  defp initialize_transformer_encoder(num_layers, dims, heads) do
    %{
      layers: initialize_transformer_layers(num_layers, dims, heads),
      final_norm: initialize_layer_norm(dims)
    }
  end
  
  # Symbolic System Initialization
  
  defp initialize_knowledge_base do
    %{
      facts: initialize_fact_base(),
      rules: initialize_rule_base(),
      ontology: initialize_ontology(),
      axioms: initialize_axioms()
    }
  end
  
  defp initialize_fact_base do
    # Basic logical facts for reasoning
    %{
      "agent(X)" => %{
        type: :compound,
        functor: "agent",
        args: [%{type: :variable, functor: "X", args: [], variables: ["X"], constraints: []}],
        variables: ["X"],
        constraints: []
      },
      "autonomous(X) :- agent(X), self_directed(X)" => %{
        type: :compound,
        functor: ":-",
        args: [
          %{type: :compound, functor: "autonomous", args: [%{type: :variable, functor: "X", args: [], variables: ["X"], constraints: []}], variables: ["X"], constraints: []},
          %{type: :compound, functor: ",", args: [
            %{type: :compound, functor: "agent", args: [%{type: :variable, functor: "X", args: [], variables: ["X"], constraints: []}], variables: ["X"], constraints: []},
            %{type: :compound, functor: "self_directed", args: [%{type: :variable, functor: "X", args: [], variables: ["X"], constraints: []}], variables: ["X"], constraints: []}
          ], variables: ["X"], constraints: []}
        ],
        variables: ["X"],
        constraints: []
      }
    }
  end
  
  defp initialize_rule_base do
    %{
      modus_ponens: %{
        name: "modus_ponens",
        pattern: {"{P → Q, P}", "Q"},
        confidence: 1.0,
        priority: 10
      },
      universal_instantiation: %{
        name: "universal_instantiation", 
        pattern: {"∀x.P(x)", "P(c)"},
        confidence: 1.0,
        priority: 9
      },
      resolution: %{
        name: "resolution",
        pattern: {"{P ∨ Q, ¬P ∨ R}", "Q ∨ R"},
        confidence: 0.95,
        priority: 8
      }
    }
  end
  
  defp initialize_ontology do
    %{
      concepts: %{
        "Agent" => %{
          properties: ["autonomous", "reactive", "social"],
          relations: ["interacts_with", "coordinates_with"],
          parent_concepts: ["Entity"],
          child_concepts: ["AIAgent", "HumanAgent"]
        },
        "Goal" => %{
          properties: ["achievable", "measurable"],
          relations: ["pursued_by", "conflicts_with"],
          parent_concepts: ["Intention"],
          child_concepts: ["LearningGoal", "PerformanceGoal"]
        }
      },
      relations: %{
        "interacts_with" => %{
          domain: "Agent",
          range: "Agent", 
          properties: ["symmetric", "reflexive"]
        },
        "pursues" => %{
          domain: "Agent",
          range: "Goal",
          properties: ["functional"]
        }
      }
    }
  end
  
  defp initialize_axioms do
    [
      # Reflexivity of agent identity
      "∀x.(agent(x) → x = x)",
      # Autonomy preservation
      "∀x.(autonomous(x) → ∃g.(goal(g) ∧ pursues(x,g)))",
      # Social interaction reciprocity
      "∀x,y.(interacts_with(x,y) → interacts_with(y,x))"
    ]
  end
  
  defp initialize_inference_engine do
    %{
      forward_chaining: %{
        enabled: true,
        max_iterations: 100,
        conflict_resolution: :priority_order
      },
      backward_chaining: %{
        enabled: true,
        max_depth: @max_proof_depth,
        search_strategy: :depth_first
      },
      resolution_prover: %{
        enabled: true,
        clause_selection: :unit_resolution,
        subsumption: true
      }
    }
  end
  
  defp initialize_proof_search do
    %{
      search_strategy: :best_first,
      heuristics: [:goal_distance, :premise_support, :rule_confidence],
      beam_width: 10,
      max_iterations: @inference_steps
    }
  end
  
  defp initialize_semantic_knowledge do
    %{
      "causality" => %{
        type: :compound,
        functor: "causes",
        args: [
          %{type: :variable, functor: "X", args: [], variables: ["X"], constraints: []},
          %{type: :variable, functor: "Y", args: [], variables: ["Y"], constraints: []}
        ],
        variables: ["X", "Y"],
        constraints: [
          %{type: :compound, functor: "precedes", args: [
            %{type: :variable, functor: "X", args: [], variables: ["X"], constraints: []},
            %{type: :variable, functor: "Y", args: [], variables: ["Y"], constraints: []}
          ], variables: ["X", "Y"], constraints: []}
        ]
      }
    }
  end
  
  defp initialize_meta_cognition do
    %{
      self_model: %{
        type: :compound,
        functor: "reasoning_agent",
        args: [
          %{type: :atom, functor: "self", args: [], variables: [], constraints: []}
        ],
        variables: [],
        constraints: []
      },
      uncertainty_estimates: %{},
      reasoning_strategies: [
        "logical_deduction",
        "analogical_reasoning", 
        "causal_inference",
        "pattern_matching",
        "meta_reasoning"
      ]
    }
  end
  
  defp initialize_integration_layer do
    %{
      attention_fusion: %{
        neural_weight: 0.6,
        symbolic_weight: 0.4,
        fusion_mechanism: :weighted_sum
      },
      consistency_checker: %{
        enabled: true,
        tolerance: 0.1,
        resolution_strategy: :neural_dominance
      },
      explanation_generator: %{
        template_based: true,
        natural_language: true,
        causal_chains: true
      }
    }
  end
  
  # Core Reasoning Implementation
  
  defp perform_reasoning(input, context, state) do
    try do
      # Stage 1: Neural encoding
      neural_encoding = encode_input_neurally(input, context, state.neural_networks)
      
      # Stage 2: Symbolic parsing
      symbolic_representation = parse_input_symbolically(input, state.symbolic_systems)
      
      # Stage 3: Multi-modal attention
      attention_weights = compute_cross_modal_attention(
        neural_encoding, 
        symbolic_representation, 
        state.neural_networks.transformer
      )
      
      # Stage 4: Parallel processing
      neural_reasoning = perform_neural_reasoning(neural_encoding, state.neural_networks)
      symbolic_reasoning = perform_symbolic_reasoning(symbolic_representation, state.symbolic_systems)
      
      # Stage 5: Integration and consistency
      integrated_result = integrate_reasoning_modes(
        neural_reasoning,
        symbolic_reasoning,
        attention_weights,
        state.integration_layer
      )
      
      # Stage 6: Generate trace
      trace = %{
        input: input,
        neural_activations: extract_neural_activations(neural_reasoning),
        symbolic_derivations: extract_symbolic_derivations(symbolic_reasoning),
        attention_patterns: attention_weights,
        final_conclusion: integrated_result.conclusion,
        confidence: integrated_result.confidence,
        explanation: generate_integrated_explanation(integrated_result, state)
      }
      
      {:ok, trace}
    catch
      error -> {:error, error}
    end
  end
  
  defp encode_input_neurally(input, context, neural_networks) do
    # Tokenize and embed input
    tokens = tokenize_input(input)
    embeddings = lookup_embeddings(tokens, neural_networks.transformer.embedding_layer)
    
    # Add positional encoding
    pos_embeddings = add_positional_encoding(embeddings, neural_networks.transformer.positional_encoding)
    
    # Apply transformer encoder
    encoded = apply_transformer_encoder(pos_embeddings, neural_networks.transformer.encoder_layers)
    
    # Apply context attention if provided
    if context != %{} do
      context_encoded = encode_context(context, neural_networks.transformer)
      apply_cross_attention(encoded, context_encoded, neural_networks.transformer)
    else
      encoded
    end
  end
  
  defp parse_input_symbolically(input, symbolic_systems) do
    # Convert input to logical representation
    logical_form = parse_to_logical_form(input)
    
    # Apply knowledge base expansion
    expanded_form = expand_with_knowledge_base(logical_form, symbolic_systems.knowledge_base)
    
    # Normalize and prepare for reasoning
    normalize_symbolic_expression(expanded_form)
  end
  
  defp compute_cross_modal_attention(neural_encoding, symbolic_repr, transformer) do
    # Compute attention between neural and symbolic representations
    neural_query = apply_linear_layer(neural_encoding, transformer.multi_head_attention.query_projection)
    symbolic_key = encode_symbolic_as_neural(symbolic_repr, transformer.embedding_layer)
    symbolic_value = symbolic_key
    
    # Multi-head attention computation
    attention_scores = compute_attention_scores(neural_query, symbolic_key, transformer.multi_head_attention.scale)
    attention_weights = apply_softmax(attention_scores)
    
    %{
      1 => %{
        query: neural_query,
        key: symbolic_key,
        value: symbolic_value,
        output: apply_attention_weights(attention_weights, symbolic_value)
      }
    }
  end
  
  defp perform_neural_reasoning(encoded_input, neural_networks) do
    # Graph neural network reasoning
    graph_features = apply_graph_network(encoded_input, neural_networks.graph_net)
    
    # Meta-cognitive network
    meta_features = apply_meta_network(encoded_input, neural_networks.meta_net)
    
    # Combine and project to output
    combined = combine_neural_features(graph_features, meta_features)
    output_logits = apply_output_projection(combined, neural_networks.transformer.output_projection)
    
    %{
      graph_reasoning: graph_features,
      meta_reasoning: meta_features,
      combined_output: combined,
      final_logits: output_logits,
      confidence: compute_neural_confidence(output_logits)
    }
  end
  
  defp perform_symbolic_reasoning(symbolic_input, symbolic_systems) do
    # Forward chaining inference
    forward_results = apply_forward_chaining(symbolic_input, symbolic_systems.inference_engine, symbolic_systems.knowledge_base)
    
    # Backward chaining for goal-directed reasoning
    backward_results = apply_backward_chaining(symbolic_input, symbolic_systems.inference_engine, symbolic_systems.knowledge_base)
    
    # Resolution-based theorem proving
    resolution_results = apply_resolution_proving(symbolic_input, symbolic_systems.inference_engine, symbolic_systems.knowledge_base)
    
    # Combine results
    %{
      forward_chain: forward_results,
      backward_chain: backward_results,
      resolution: resolution_results,
      final_conclusions: merge_symbolic_results([forward_results, backward_results, resolution_results]),
      proof_confidence: compute_symbolic_confidence([forward_results, backward_results, resolution_results])
    }
  end
  
  defp integrate_reasoning_modes(neural_result, symbolic_result, attention_weights, integration_layer) do
    # Weighted combination based on confidence
    neural_confidence = neural_result.confidence
    symbolic_confidence = symbolic_result.proof_confidence
    
    # Normalize confidences
    total_confidence = neural_confidence + symbolic_confidence
    neural_weight = if total_confidence > 0, do: neural_confidence / total_confidence, else: 0.5
    symbolic_weight = 1.0 - neural_weight
    
    # Combine conclusions
    integrated_conclusion = combine_conclusions(
      neural_result.combined_output,
      symbolic_result.final_conclusions,
      neural_weight,
      symbolic_weight,
      attention_weights
    )
    
    # Check consistency
    consistency_score = check_consistency(neural_result, symbolic_result, integration_layer.consistency_checker)
    
    %{
      conclusion: integrated_conclusion,
      confidence: (neural_confidence * neural_weight + symbolic_confidence * symbolic_weight) * consistency_score,
      neural_weight: neural_weight,
      symbolic_weight: symbolic_weight,
      consistency: consistency_score
    }
  end
  
  # Helper Functions
  
  defp extract_neural_parameters(neural_networks) do
    neural_networks
    |> Enum.map(fn {name, network} -> {name, extract_parameters_from_network(network)} end)
    |> Map.new()
  end
  
  defp extract_parameters_from_network(network) when is_map(network) do
    network
    |> Enum.filter(fn {_key, value} -> is_map(value) and Map.has_key?(value, :data) end)
    |> Map.new()
  end
  defp extract_parameters_from_network(_), do: %{}
  
  defp update_cognitive_state(cognitive_state, trace) do
    # Update episodic memory
    new_episodic = Map.put(cognitive_state.episodic_memory, 
      :crypto.hash(:sha256, :erlang.term_to_binary(trace.input)), trace)
    
    # Update uncertainty estimates
    new_uncertainties = Map.put(cognitive_state.meta_cognition.uncertainty_estimates,
      trace.input, 1.0 - trace.confidence)
    
    new_meta_cognition = %{cognitive_state.meta_cognition | 
      uncertainty_estimates: new_uncertainties
    }
    
    %{cognitive_state | 
      episodic_memory: new_episodic,
      meta_cognition: new_meta_cognition
    }
  end
  
  defp update_moving_average(current, new_value, alpha \\ 0.1) do
    if current == 0.0 do
      new_value
    else
      alpha * new_value + (1 - alpha) * current
    end
  end
  
  # Simplified implementations for core functions
  
  defp tokenize_input(input) do
    input |> to_string() |> String.split() |> Enum.map(&String.downcase/1)
  end
  
  defp lookup_embeddings(tokens, embedding_layer) do
    # Simplified embedding lookup
    %{data: List.duplicate(0.5, length(tokens) * @transformer_dims), 
      shape: [length(tokens), @transformer_dims], dtype: :float32}
  end
  
  defp add_positional_encoding(embeddings, pos_encoding) do
    embeddings  # Simplified - just return embeddings
  end
  
  defp apply_transformer_encoder(input, encoder_layers) do
    input  # Simplified - return input
  end
  
  defp encode_context(context, transformer) do
    %{data: List.duplicate(0.3, @transformer_dims), shape: [@transformer_dims], dtype: :float32}
  end
  
  defp apply_cross_attention(encoded, context_encoded, transformer) do
    encoded  # Simplified
  end
  
  defp parse_to_logical_form(input) do
    %{
      type: :compound,
      functor: "query",
      args: [%{type: :atom, functor: to_string(input), args: [], variables: [], constraints: []}],
      variables: [],
      constraints: []
    }
  end
  
  defp expand_with_knowledge_base(logical_form, knowledge_base) do
    logical_form  # Simplified
  end
  
  defp normalize_symbolic_expression(expr) do
    expr  # Simplified
  end
  
  defp encode_symbolic_as_neural(symbolic_repr, embedding_layer) do
    %{data: List.duplicate(0.4, @transformer_dims), shape: [@transformer_dims], dtype: :float32}
  end
  
  defp compute_attention_scores(query, key, scale) do
    %{data: [0.8, 0.2], shape: [2], dtype: :float32}
  end
  
  defp apply_softmax(scores) do
    scores  # Simplified
  end
  
  defp apply_attention_weights(weights, values) do
    values  # Simplified
  end
  
  defp apply_graph_network(input, graph_net) do
    input  # Simplified
  end
  
  defp apply_meta_network(input, meta_net) do
    input  # Simplified
  end
  
  defp combine_neural_features(graph_features, meta_features) do
    graph_features  # Simplified
  end
  
  defp apply_output_projection(features, projection) do
    features  # Simplified
  end
  
  defp compute_neural_confidence(logits) do
    0.75  # Simplified
  end
  
  defp apply_forward_chaining(input, inference_engine, knowledge_base) do
    []  # Simplified
  end
  
  defp apply_backward_chaining(input, inference_engine, knowledge_base) do
    []  # Simplified
  end
  
  defp apply_resolution_proving(input, inference_engine, knowledge_base) do
    []  # Simplified
  end
  
  defp merge_symbolic_results(results) do
    List.flatten(results)
  end
  
  defp compute_symbolic_confidence(results) do
    0.65  # Simplified
  end
  
  defp combine_conclusions(neural_output, symbolic_conclusions, neural_weight, symbolic_weight, attention_weights) do
    "integrated_conclusion_#{neural_weight}_#{symbolic_weight}"  # Simplified
  end
  
  defp check_consistency(neural_result, symbolic_result, consistency_checker) do
    0.9  # Simplified consistency score
  end
  
  defp extract_neural_activations(neural_reasoning) do
    %{1 => neural_reasoning.combined_output}
  end
  
  defp extract_symbolic_derivations(symbolic_reasoning) do
    symbolic_reasoning.final_conclusions |> Enum.map(fn conclusion ->
      %{
        rule: "simplified_rule",
        premises: [],
        conclusion: conclusion,
        justification: "automated_inference",
        confidence: 0.8
      }
    end)
  end
  
  defp generate_integrated_explanation(integrated_result, state) do
    "Neural-symbolic reasoning produced: #{inspect(integrated_result.conclusion)} with confidence #{integrated_result.confidence}"
  end
  
  defp update_from_feedback(trace, feedback, state) do
    # Simplified learning update
    {:ok, state}
  end
  
  defp perform_causal_inference(query, evidence, state) do
    # Simplified causal inference
    {:ok, [query]}
  end
  
  defp generate_explanation(trace, state) do
    "Reasoning trace explanation: #{inspect(trace.final_conclusion)}"
  end
  
  defp perform_meta_reflection(state) do
    insights = %{
      reasoning_efficiency: state.performance_metrics.inference_speed,
      knowledge_gaps: [],
      strategy_effectiveness: %{}
    }
    {:ok, insights, state}
  end
  
  defp perform_few_shot_learning(examples, query, state) do
    # Simplified few-shot learning
    if length(examples) > 0 do
      {_input, output} = hd(examples)
      {:ok, output}
    else
      {:ok, "no_examples"}
    end
  end
  
  defp apply_linear_layer(input, layer) do
    input  # Simplified
  end
end