object

defmodule Object.TransferLearning do
  @moduledoc """
  Object-Oriented Transfer Learning mechanisms for OORL framework.
  
  Implements transfer learning capabilities as specified in AAOS section 11,
  enabling objects to leverage prior experience and knowledge to learn faster
  and generalize better to new tasks and domains.
  
  Key mechanisms:
  - Object similarity and embedding spaces
  - Analogical reasoning between objects and domains
  - Meta-learning for rapid adaptation
  - Knowledge distillation between objects
  - Cross-domain policy transfer
  """

  defstruct [
    :object_id,
    :embedding_space,
    :similarity_metrics,
    :analogy_engine,
    :meta_learning_state,
    :transfer_history,
    :knowledge_base,
    :domain_mappings
  ]

  @type t :: %__MODULE__{
    object_id: String.t(),
    embedding_space: embedding_space(),
    similarity_metrics: [similarity_metric()],
    analogy_engine: analogy_engine(),
    meta_learning_state: meta_learning_state(),
    transfer_history: [transfer_record()],
    knowledge_base: knowledge_base(),
    domain_mappings: %{domain_id() => domain_mapping()}
  }

  @type embedding_space :: %{
    dimensions: integer(),
    object_embeddings: %{object_id() => embedding_vector()},
    domain_embeddings: %{domain_id() => embedding_vector()},
    task_embeddings: %{task_id() => embedding_vector()},
    embedding_model: embedding_model()
  }

  @type similarity_metric :: %{
    name: atom(),
    weight: float(),
    metric_function: function()
  }

  @type analogy_engine :: %{
    analogy_templates: [analogy_template()],
    mapping_rules: [mapping_rule()],
    abstraction_levels: [abstraction_level()],
    analogy_cache: %{analogy_key() => analogy_result()}
  }

  @type meta_learning_state :: %{
    adaptation_parameters: map(),
    learning_to_learn_history: [learning_episode()],
    meta_gradients: map(),
    adaptation_strategies: [adaptation_strategy()]
  }

  @type transfer_record :: %{
    timestamp: DateTime.t(),
    source_domain: domain_id(),
    target_domain: domain_id(),
    transfer_method: transfer_method(),
    success_metric: float(),
    knowledge_transferred: term(),
    adaptation_steps: integer()
  }

  @type knowledge_base :: %{
    declarative_knowledge: map(),
    procedural_knowledge: [procedure()],
    episodic_knowledge: [episode()],
    semantic_knowledge: map()
  }

  @type domain_mapping :: %{
    domain_id: domain_id(),
    feature_mapping: %{feature_id() => feature_id()},
    action_mapping: %{action_id() => action_id()},
    similarity_score: float(),
    transfer_compatibility: float()
  }

  @type embedding_vector :: [float()]
  @type embedding_model :: atom()
  @type analogy_template :: map()
  @type mapping_rule :: map()
  @type abstraction_level :: integer()
  @type analogy_key :: term()
  @type analogy_result :: map()
  @type learning_episode :: map()
  @type adaptation_strategy :: map()
  @type transfer_method :: atom()
  @type procedure :: map()
  @type episode :: map()
  @type domain_id :: String.t()
  @type task_id :: String.t()
  @type object_id :: String.t()
  @type feature_id :: String.t()
  @type action_id :: String.t()

  @doc """
  Creates a new transfer learning system for an object.
  
  ## Parameters
  
  - `object_id` - Unique identifier for the object
  - `opts` - Configuration options:
    - `:embedding_dimensions` - Size of embedding vectors (default: 64)
    - `:embedding_model` - Type of embedding model (default: :neural_embedding)
  
  ## Returns
  
  New transfer learning system struct with initialized components
  
  ## Examples
  
      iex> Object.TransferLearning.new("agent_1", embedding_dimensions: 128)
      %Object.TransferLearning{object_id: "agent_1", ...}
  """
  def new(object_id, opts \\ []) do
    %__MODULE__{
      object_id: object_id,
      embedding_space: initialize_embedding_space(opts),
      similarity_metrics: initialize_similarity_metrics(opts),
      analogy_engine: initialize_analogy_engine(opts),
      meta_learning_state: initialize_meta_learning_state(opts),
      transfer_history: [],
      knowledge_base: initialize_knowledge_base(opts),
      domain_mappings: %{}
    }
  end

  @doc """
  Computes similarity between two objects using multiple metrics.
  
  ## Parameters
  
  - `transfer_system` - Transfer learning system struct
  - `source_object` - First object for comparison
  - `target_object` - Second object for comparison
  
  ## Returns
  
  Map containing:
  - `:overall_similarity` - Weighted average similarity score (0.0-1.0)
  - `:detailed_scores` - Individual metric scores
  - `:confidence` - Confidence in similarity measurement
  """
  def compute_object_similarity(%__MODULE__{} = transfer_system, source_object, target_object) do
    similarity_scores = for metric <- transfer_system.similarity_metrics do
      score = metric.metric_function.(source_object, target_object)
      {metric.name, score * metric.weight}
    end
    
    total_weight = Enum.sum(Enum.map(transfer_system.similarity_metrics, & &1.weight))
    weighted_average = Enum.sum(Enum.map(similarity_scores, &elem(&1, 1))) / total_weight
    
    %{
      overall_similarity: weighted_average,
      detailed_scores: Map.new(similarity_scores),
      confidence: calculate_similarity_confidence(similarity_scores)
    }
  end

  @doc """
  Identifies transfer opportunities from source to target domain.
  
  ## Parameters
  
  - `transfer_system` - Transfer learning system struct
  - `source_domain` - Source domain specification
  - `target_domain` - Target domain specification
  
  ## Returns
  
  Map with comprehensive transfer analysis:
  - `:domain_similarity` - Similarity metrics between domains
  - `:analogical_mappings` - Structural correspondences found
  - `:transfer_feasibility` - Assessment of transfer viability
  - `:recommendations` - Specific transfer method recommendations
  - `:estimated_benefit` - Expected benefit of transfer
  """
  def identify_transfer_opportunities(%__MODULE__{} = transfer_system, source_domain, target_domain) do
    # Analyze domain similarity
    domain_similarity = compute_domain_similarity(transfer_system, source_domain, target_domain)
    
    # Find analogical mappings
    analogical_mappings = find_analogical_mappings(transfer_system, source_domain, target_domain)
    
    # Assess transfer feasibility
    transfer_feasibility = assess_transfer_feasibility(transfer_system, source_domain, target_domain)
    
    # Generate transfer recommendations
    recommendations = generate_transfer_recommendations(domain_similarity, analogical_mappings, transfer_feasibility)
    
    %{
      domain_similarity: domain_similarity,
      analogical_mappings: analogical_mappings,
      transfer_feasibility: transfer_feasibility,
      recommendations: recommendations,
      estimated_benefit: estimate_transfer_benefit(transfer_system, source_domain, target_domain)
    }
  end

  @doc """
  Performs analogical reasoning to find structural correspondences.
  
  ## Parameters
  
  - `transfer_system` - Transfer learning system struct
  - `source_structure` - Source structure for analogy
  - `target_structure` - Target structure for analogy
  
  ## Returns
  
  Map containing:
  - `:correspondences` - Structural element mappings
  - `:template_matches` - Template-based analogy matches
  - `:inferences` - Generated analogical inferences
  - `:confidence` - Overall confidence in analogical reasoning
  """
  def perform_analogical_reasoning(%__MODULE__{} = transfer_system, source_structure, target_structure) do
    analogy_engine = transfer_system.analogy_engine
    
    # Find structural correspondences
    correspondences = find_structural_correspondences(analogy_engine, source_structure, target_structure)
    
    # Apply analogy templates
    template_matches = apply_analogy_templates(analogy_engine, source_structure, target_structure)
    
    # Generate analogical inferences
    inferences = generate_analogical_inferences(correspondences, template_matches)
    
    %{
      correspondences: correspondences,
      template_matches: template_matches,
      inferences: inferences,
      confidence: calculate_analogy_confidence(correspondences, template_matches)
    }
  end

  @doc """
  Executes meta-learning for rapid adaptation to new tasks.
  
  ## Parameters
  
  - `transfer_system` - Transfer learning system struct
  - `adaptation_task` - Task specification for adaptation
  - `few_shot_examples` - Limited examples for rapid learning
  
  ## Returns
  
  - `{:ok, adapted_parameters, updated_system}` - Success with adapted parameters
  - `{:error, reason}` - Adaptation failed
  """
  def meta_learn(%__MODULE__{} = transfer_system, adaptation_task, few_shot_examples) do
    meta_state = transfer_system.meta_learning_state
    
    # Apply meta-learning algorithm (simplified MAML-style approach)
    {:ok, adapted_parameters} = apply_meta_learning_algorithm(meta_state, adaptation_task, few_shot_examples)
    
    # Update meta-learning state
    updated_meta_state = update_meta_learning_state(meta_state, adaptation_task, adapted_parameters)
    updated_transfer_system = %{transfer_system | meta_learning_state: updated_meta_state}
    
    {:ok, adapted_parameters, updated_transfer_system}
  end

  @doc """
  Transfers knowledge from source object to target object.
  
  ## Parameters
  
  - `transfer_system` - Transfer learning system struct
  - `source_object` - Object providing knowledge
  - `target_object` - Object receiving knowledge
  - `transfer_method` - Method to use (`:automatic`, `:policy_distillation`, `:feature_mapping`, `:analogical`, `:meta_learning`)
  
  ## Returns
  
  - `{:ok, transferred_knowledge, updated_system}` - Success with transferred knowledge
  - `{:error, reason}` - Transfer failed
  """
  def transfer_knowledge(%__MODULE__{} = transfer_system, source_object, target_object, transfer_method \\ :automatic) do
    case transfer_method do
      :automatic ->
        automatic_knowledge_transfer(transfer_system, source_object, target_object)
      
      :policy_distillation ->
        policy_distillation_transfer(transfer_system, source_object, target_object)
      
      :feature_mapping ->
        feature_mapping_transfer(transfer_system, source_object, target_object)
      
      :analogical ->
        analogical_knowledge_transfer(transfer_system, source_object, target_object)
      
      :meta_learning ->
        meta_learning_transfer(transfer_system, source_object, target_object)
      
      _ ->
        {:error, {:unknown_transfer_method, transfer_method}}
    end
  end

  @doc """
  Updates object embeddings in the shared embedding space.
  
  ## Parameters
  
  - `transfer_system` - Transfer learning system struct
  - `object` - Object whose embedding should be updated
  - `new_experiences` - Recent experiences to incorporate
  
  ## Returns
  
  Updated transfer learning system with modified embedding space
  """
  def update_object_embedding(%__MODULE__{} = transfer_system, object, new_experiences) do
    current_embedding = Map.get(transfer_system.embedding_space.object_embeddings, object.id, random_embedding())
    
    # Update embedding based on new experiences
    updated_embedding = update_embedding_from_experiences(current_embedding, new_experiences)
    
    # Update embedding space
    updated_embeddings = Map.put(transfer_system.embedding_space.object_embeddings, object.id, updated_embedding)
    updated_embedding_space = %{transfer_system.embedding_space | object_embeddings: updated_embeddings}
    
    %{transfer_system | embedding_space: updated_embedding_space}
  end

  @doc """
  Evaluates the effectiveness of transfer learning.
  
  ## Parameters
  
  - `transfer_system` - Transfer learning system struct
  
  ## Returns
  
  Map containing:
  - `:overall_effectiveness` - Aggregate effectiveness score (0.0-1.0)
  - `:detailed_metrics` - Individual performance metrics
  - `:recommendations` - Improvement recommendations
  """
  def evaluate_transfer_effectiveness(%__MODULE__{} = transfer_system) do
    recent_transfers = Enum.take(transfer_system.transfer_history, 20)
    
    if length(recent_transfers) > 0 do
      metrics = %{
        average_success_rate: calculate_average_success_rate(recent_transfers),
        adaptation_efficiency: calculate_adaptation_efficiency(recent_transfers),
        knowledge_retention: calculate_knowledge_retention(transfer_system),
        transfer_diversity: calculate_transfer_diversity(recent_transfers),
        meta_learning_progress: calculate_meta_learning_progress(transfer_system.meta_learning_state)
      }
      
      overall_effectiveness = aggregate_transfer_metrics(metrics)
      
      %{
        overall_effectiveness: overall_effectiveness,
        detailed_metrics: metrics,
        recommendations: generate_transfer_recommendations_from_metrics(metrics)
      }
    else
      %{
        overall_effectiveness: 0.0,
        detailed_metrics: %{},
        recommendations: ["Collect more transfer learning data"]
      }
    end
  end

  # Private implementation functions

  defp initialize_embedding_space(opts) do
    dimensions = Keyword.get(opts, :embedding_dimensions, 64)
    
    %{
      dimensions: dimensions,
      object_embeddings: %{},
      domain_embeddings: %{},
      task_embeddings: %{},
      embedding_model: Keyword.get(opts, :embedding_model, :neural_embedding)
    }
  end

  defp initialize_similarity_metrics(_opts) do
    [
      %{
        name: :state_similarity,
        weight: 0.3,
        metric_function: &compute_state_similarity/2
      },
      %{
        name: :behavioral_similarity,
        weight: 0.4,
        metric_function: &compute_behavioral_similarity/2
      },
      %{
        name: :goal_similarity,
        weight: 0.2,
        metric_function: &compute_goal_similarity/2
      },
      %{
        name: :embedding_similarity,
        weight: 0.1,
        metric_function: &compute_embedding_similarity/2
      }
    ]
  end

  defp initialize_analogy_engine(_opts) do
    %{
      analogy_templates: create_default_analogy_templates(),
      mapping_rules: create_default_mapping_rules(),
      abstraction_levels: [0, 1, 2, 3],  # Different levels of abstraction
      analogy_cache: %{}
    }
  end

  defp initialize_meta_learning_state(_opts) do
    %{
      adaptation_parameters: %{
        inner_lr: 0.01,
        outer_lr: 0.001,
        adaptation_steps: 5
      },
      learning_to_learn_history: [],
      meta_gradients: %{},
      adaptation_strategies: create_default_adaptation_strategies()
    }
  end

  defp initialize_knowledge_base(_opts) do
    %{
      declarative_knowledge: %{},
      procedural_knowledge: [],
      episodic_knowledge: [],
      semantic_knowledge: %{}
    }
  end

  defp compute_domain_similarity(transfer_system, source_domain, target_domain) do
    # Compare domain embeddings if available
    source_embedding = Map.get(transfer_system.embedding_space.domain_embeddings, source_domain)
    target_embedding = Map.get(transfer_system.embedding_space.domain_embeddings, target_domain)
    
    embedding_similarity = if source_domain == target_domain do
      1.0  # Perfect similarity for identical domains
    else
      if source_embedding && target_embedding do
        cosine_similarity(source_embedding, target_embedding)
      else
        0.5  # Default similarity when embeddings not available
      end
    end
    
    # Additional domain similarity metrics
    feature_overlap = calculate_feature_overlap(source_domain, target_domain)
    structural_similarity = calculate_structural_similarity(source_domain, target_domain)
    
    %{
      embedding_similarity: embedding_similarity,
      feature_overlap: feature_overlap,
      structural_similarity: structural_similarity,
      overall_similarity: (embedding_similarity + feature_overlap + structural_similarity) / 3
    }
  end

  defp find_analogical_mappings(transfer_system, source_domain, target_domain) do
    analogy_engine = transfer_system.analogy_engine
    
    # Apply mapping rules to find correspondences
    mappings = for rule <- analogy_engine.mapping_rules do
      apply_mapping_rule(rule, source_domain, target_domain)
    end
    |> Enum.reject(&is_nil/1)
    
    # Filter and rank mappings by confidence
    Enum.sort_by(mappings, & &1.confidence, :desc)
  end

  defp assess_transfer_feasibility(transfer_system, source_domain, target_domain) do
    # Check historical transfer success between similar domains
    historical_success = get_historical_transfer_success(transfer_system, source_domain, target_domain)
    
    # Assess computational cost
    transfer_cost = estimate_transfer_cost(source_domain, target_domain)
    
    # Check domain compatibility
    compatibility = assess_domain_compatibility(source_domain, target_domain)
    
    %{
      historical_success: historical_success,
      transfer_cost: transfer_cost,
      domain_compatibility: compatibility,
      overall_feasibility: (historical_success + compatibility - transfer_cost) / 2
    }
  end

  defp generate_transfer_recommendations(domain_similarity, analogical_mappings, transfer_feasibility) do
    recommendations = []
    
    recommendations = if domain_similarity.overall_similarity > 0.7 do
      ["High domain similarity detected - direct transfer recommended" | recommendations]
    else
      recommendations
    end
    
    recommendations = if length(analogical_mappings) > 3 do
      ["Strong analogical mappings found - analogical transfer recommended" | recommendations]
    else
      recommendations
    end
    
    recommendations = if transfer_feasibility.overall_feasibility > 0.6 do
      ["Transfer appears feasible with good success probability" | recommendations]
    else
      ["Transfer may be challenging - consider meta-learning approach" | recommendations]
    end
    
    recommendations
  end

  defp automatic_knowledge_transfer(transfer_system, source_object, target_object) do
    # Determine best transfer method automatically
    similarity = compute_object_similarity(transfer_system, source_object, target_object)
    
    selected_method = cond do
      similarity.overall_similarity > 0.8 ->
        :policy_distillation
      
      similarity.overall_similarity > 0.6 ->
        :feature_mapping
      
      similarity.overall_similarity > 0.4 ->
        :analogical
      
      true ->
        :meta_learning
    end
    
    # Execute the selected method but record as automatic
    case transfer_knowledge(transfer_system, source_object, target_object, selected_method) do
      {:ok, transferred_knowledge, updated_system} ->
        # Update the transfer record to show :automatic as the method
        [latest_record | rest] = updated_system.transfer_history
        updated_record = %{latest_record | transfer_method: :automatic}
        updated_system = %{updated_system | transfer_history: [updated_record | rest]}
        
        {:ok, transferred_knowledge, updated_system}
        
      error ->
        error
    end
  end

  defp policy_distillation_transfer(transfer_system, source_object, target_object) do
    # Extract source policy knowledge
    source_policy = extract_policy_knowledge(source_object)
    
    # Adapt policy to target object's capabilities
    adapted_policy = adapt_policy_to_target(source_policy, target_object)
    
    # Record transfer
    transfer_record = create_transfer_record(:policy_distillation, source_object, target_object, 0.8)
    updated_history = [transfer_record | transfer_system.transfer_history]
    
    {:ok, adapted_policy, %{transfer_system | transfer_history: updated_history}}
  end

  defp feature_mapping_transfer(transfer_system, source_object, target_object) do
    # Map features between source and target domains
    feature_mapping = create_feature_mapping(source_object, target_object)
    
    # Transfer mapped features
    transferred_features = apply_feature_mapping(source_object, feature_mapping)
    
    # Record transfer
    transfer_record = create_transfer_record(:feature_mapping, source_object, target_object, 0.7)
    updated_history = [transfer_record | transfer_system.transfer_history]
    
    {:ok, transferred_features, %{transfer_system | transfer_history: updated_history}}
  end

  defp analogical_knowledge_transfer(transfer_system, source_object, target_object) do
    # Perform analogical reasoning
    analogical_result = perform_analogical_reasoning(transfer_system, source_object, target_object)
    
    # Extract transferable knowledge from analogies
    transferred_knowledge = extract_analogical_knowledge(analogical_result)
    
    # Record transfer
    transfer_record = create_transfer_record(:analogical, source_object, target_object, analogical_result.confidence)
    updated_history = [transfer_record | transfer_system.transfer_history]
    
    {:ok, transferred_knowledge, %{transfer_system | transfer_history: updated_history}}
  end

  defp meta_learning_transfer(transfer_system, source_object, target_object) do
    # Use meta-learning for rapid adaptation
    adaptation_task = create_adaptation_task(source_object, target_object)
    few_shot_examples = generate_few_shot_examples(source_object, target_object)
    
    {:ok, adapted_parameters, updated_transfer_system} = meta_learn(transfer_system, adaptation_task, few_shot_examples)
    transfer_record = create_transfer_record(:meta_learning, source_object, target_object, 0.6)
    final_transfer_system = %{updated_transfer_system | transfer_history: [transfer_record | updated_transfer_system.transfer_history]}
    
    {:ok, adapted_parameters, final_transfer_system}
  end

  # Simplified helper functions for demo

  defp random_embedding(dimensions \\ 64) do
    for _ <- 1..dimensions, do: :rand.uniform() * 2 - 1
  end

  defp compute_state_similarity(obj1, obj2) do
    # Simplified state similarity computation
    state1 = Map.get(obj1, :state, %{})
    state2 = Map.get(obj2, :state, %{})
    
    common_keys = Map.keys(state1) -- (Map.keys(state1) -- Map.keys(state2))
    
    if length(common_keys) > 0 do
      similarities = for key <- common_keys do
        val1 = Map.get(state1, key)
        val2 = Map.get(state2, key)
        if val1 == val2, do: 1.0, else: 0.5
      end
      Enum.sum(similarities) / length(similarities)
    else
      0.0
    end
  end

  defp compute_behavioral_similarity(obj1, obj2) do
    # Simplified behavioral similarity
    methods1 = Map.get(obj1, :methods, [])
    methods2 = Map.get(obj2, :methods, [])
    
    intersection = MapSet.intersection(MapSet.new(methods1), MapSet.new(methods2))
    union = MapSet.union(MapSet.new(methods1), MapSet.new(methods2))
    
    if MapSet.size(union) > 0 do
      MapSet.size(intersection) / MapSet.size(union)
    else
      1.0
    end
  end

  defp compute_goal_similarity(_obj1, _obj2) do
    # Simplified goal similarity
    :rand.uniform() * 0.6 + 0.2
  end

  defp compute_embedding_similarity(obj1, obj2) do
    # Use object embeddings for similarity
    embed1 = Object.embed(obj1)
    embed2 = Object.embed(obj2)
    cosine_similarity(embed1, embed2)
  end

  defp cosine_similarity(vec1, vec2) when length(vec1) == length(vec2) do
    dot_product = Enum.zip(vec1, vec2) |> Enum.map(fn {a, b} -> a * b end) |> Enum.sum()
    norm1 = :math.sqrt(Enum.map(vec1, &(&1 * &1)) |> Enum.sum())
    norm2 = :math.sqrt(Enum.map(vec2, &(&1 * &1)) |> Enum.sum())
    
    if norm1 > 0 and norm2 > 0 do
      dot_product / (norm1 * norm2)
    else
      0.0
    end
  end

  defp cosine_similarity(_, _), do: 0.0

  defp calculate_similarity_confidence(similarity_scores) do
    scores = Enum.map(similarity_scores, &elem(&1, 1))
    variance = calculate_variance(scores)
    1.0 - min(1.0, variance)  # Lower variance = higher confidence
  end

  defp calculate_variance(numbers) do
    if length(numbers) > 1 do
      mean = Enum.sum(numbers) / length(numbers)
      sum_of_squares = Enum.map(numbers, &((&1 - mean) * (&1 - mean))) |> Enum.sum()
      sum_of_squares / length(numbers)
    else
      0.0
    end
  end

  defp estimate_transfer_benefit(_transfer_system, _source_domain, _target_domain) do
    # Simplified benefit estimation
    :rand.uniform() * 0.8 + 0.1
  end

  defp create_default_analogy_templates do
    [
      %{
        name: :structural_analogy,
        pattern: [:source_structure, :target_structure],
        mapping_type: :one_to_one
      },
      %{
        name: :functional_analogy,
        pattern: [:source_function, :target_function],
        mapping_type: :many_to_many
      }
    ]
  end

  defp create_default_mapping_rules do
    [
      %{
        name: :semantic_mapping,
        condition: fn source, target -> has_semantic_similarity?(source, target) end,
        confidence: 0.8
      }
    ]
  end

  defp create_default_adaptation_strategies do
    [
      %{name: :gradient_based, priority: 0.7},
      %{name: :evolutionary, priority: 0.3}
    ]
  end

  defp has_semantic_similarity?(_source, _target), do: true

  defp find_structural_correspondences(_analogy_engine, _source_structure, _target_structure) do
    # Simplified structural correspondence finding
    [
      %{source_element: :element1, target_element: :element_a, confidence: 0.8},
      %{source_element: :element2, target_element: :element_b, confidence: 0.6}
    ]
  end

  defp apply_analogy_templates(_analogy_engine, _source_structure, _target_structure) do
    # Simplified template application
    [
      %{template: :structural_analogy, match_score: 0.7, mappings: [:mapping1, :mapping2]}
    ]
  end

  defp generate_analogical_inferences(_correspondences, _template_matches) do
    # Simplified inference generation
    [
      %{inference: :inferred_property, confidence: 0.6}
    ]
  end

  defp calculate_analogy_confidence(_correspondences, _template_matches) do
    :rand.uniform() * 0.4 + 0.4
  end

  defp apply_meta_learning_algorithm(_meta_state, _adaptation_task, _few_shot_examples) do
    # Simplified meta-learning (MAML-style)
    adapted_params = %{
      learning_rate: 0.01,
      policy_weights: random_embedding(32)
    }
    
    {:ok, adapted_params}
  end

  defp update_meta_learning_state(meta_state, adaptation_task, adapted_parameters) do
    new_episode = %{
      task: adaptation_task,
      parameters: adapted_parameters,
      timestamp: DateTime.utc_now()
    }
    
    updated_history = [new_episode | Enum.take(meta_state.learning_to_learn_history, 99)]
    %{meta_state | learning_to_learn_history: updated_history}
  end

  defp calculate_feature_overlap(source_domain, target_domain) do
    if source_domain == target_domain do
      1.0  # Perfect overlap for identical domains
    else
      :rand.uniform() * 0.6 + 0.2
    end
  end
  
  defp calculate_structural_similarity(source_domain, target_domain) do
    if source_domain == target_domain do
      1.0  # Perfect structural similarity for identical domains
    else
      :rand.uniform() * 0.8 + 0.1
    end
  end
  defp apply_mapping_rule(_rule, _source_domain, _target_domain), do: %{confidence: :rand.uniform()}
  defp get_historical_transfer_success(_transfer_system, _source_domain, _target_domain), do: 0.6
  defp estimate_transfer_cost(_source_domain, _target_domain), do: 0.3
  defp assess_domain_compatibility(_source_domain, _target_domain), do: 0.7

  defp extract_policy_knowledge(object) do
    Map.get(object, :policy, %{default_policy: :random})
  end

  defp adapt_policy_to_target(policy, _target_object) do
    %{adapted_policy: policy, adaptation_confidence: 0.8}
  end

  defp create_feature_mapping(source_object, target_object) do
    source_features = Map.keys(Map.get(source_object, :state, %{}))
    target_features = Map.keys(Map.get(target_object, :state, %{}))
    
    # Simple one-to-one mapping
    Enum.zip(source_features, target_features) |> Map.new()
  end

  defp apply_feature_mapping(source_object, mapping) do
    source_state = Map.get(source_object, :state, %{})
    
    mapped_features = for {source_key, target_key} <- mapping do
      value = Map.get(source_state, source_key)
      {target_key, value}
    end |> Map.new()
    
    %{mapped_features: mapped_features}
  end

  defp extract_analogical_knowledge(analogical_result) do
    %{
      correspondences: analogical_result.correspondences,
      inferences: analogical_result.inferences,
      confidence: analogical_result.confidence
    }
  end

  defp create_adaptation_task(source_object, target_object) do
    %{
      source_id: source_object.id,
      target_id: target_object.id,
      task_type: :policy_adaptation
    }
  end

  defp generate_few_shot_examples(_source_object, _target_object) do
    # Generate synthetic few-shot examples
    for i <- 1..5 do
      %{example_id: i, state: %{value: i}, action: :action_a, reward: i * 0.1}
    end
  end

  defp create_transfer_record(method, source_object, target_object, success_metric) do
    %{
      timestamp: DateTime.utc_now(),
      source_domain: Map.get(source_object, :domain, "unknown"),
      target_domain: Map.get(target_object, :domain, "unknown"),
      transfer_method: method,
      success_metric: success_metric,
      knowledge_transferred: %{basic: true},
      adaptation_steps: 5
    }
  end

  defp update_embedding_from_experiences(current_embedding, _new_experiences) do
    # Simplified embedding update
    Enum.map(current_embedding, &(&1 + (:rand.uniform() - 0.5) * 0.1))
  end

  defp calculate_average_success_rate(transfers) do
    if length(transfers) > 0 do
      Enum.map(transfers, & &1.success_metric) |> Enum.sum() |> Kernel./(length(transfers))
    else
      0.0
    end
  end

  defp calculate_adaptation_efficiency(transfers) do
    if length(transfers) > 0 do
      avg_steps = Enum.map(transfers, & &1.adaptation_steps) |> Enum.sum() |> Kernel./(length(transfers))
      1.0 / (1.0 + avg_steps / 10.0)  # Efficiency decreases with more adaptation steps
    else
      0.0
    end
  end

  defp calculate_knowledge_retention(_transfer_system) do
    # Simplified retention calculation
    :rand.uniform() * 0.6 + 0.3
  end

  defp calculate_transfer_diversity(transfers) do
    methods = Enum.map(transfers, & &1.transfer_method) |> Enum.uniq()
    length(methods) / 4.0  # Assuming 4 possible methods
  end

  defp calculate_meta_learning_progress(meta_state) do
    episodes = length(meta_state.learning_to_learn_history)
    min(1.0, episodes / 50.0)  # Progress based on number of episodes
  end

  defp aggregate_transfer_metrics(metrics) do
    weights = %{
      average_success_rate: 0.3,
      adaptation_efficiency: 0.2,
      knowledge_retention: 0.2,
      transfer_diversity: 0.15,
      meta_learning_progress: 0.15
    }
    
    Enum.reduce(metrics, 0.0, fn {metric, value}, acc ->
      weight = Map.get(weights, metric, 0.0)
      acc + (value * weight)
    end)
  end

  defp generate_transfer_recommendations_from_metrics(metrics) do
    recommendations = []
    
    recommendations = if metrics.average_success_rate < 0.5 do
      ["Improve transfer method selection" | recommendations]
    else
      recommendations
    end
    
    recommendations = if metrics.adaptation_efficiency < 0.4 do
      ["Optimize adaptation algorithms" | recommendations]
    else
      recommendations
    end
    
    recommendations = if metrics.transfer_diversity < 0.5 do
      ["Explore more diverse transfer methods" | recommendations]
    else
      recommendations
    end
    
    if length(recommendations) == 0 do
      ["Transfer learning performing well"]
    else
      recommendations
    end
  end
end