Object.TransferLearning (object v0.1.2)

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

Summary

Types

abstraction_level()
action_id()
adaptation_strategy()
analogy_engine()
analogy_key()
analogy_result()
analogy_template()
domain_id()
domain_mapping()
embedding_model()
embedding_space()
embedding_vector()
episode()
feature_id()
knowledge_base()
learning_episode()
mapping_rule()
meta_learning_state()
object_id()
procedure()
similarity_metric()
t()
task_id()
transfer_method()
transfer_record()

Functions

compute_object_similarity(transfer_system, source_object, target_object)

Computes similarity between two objects using multiple metrics.

evaluate_transfer_effectiveness(transfer_system)

Evaluates the effectiveness of transfer learning.

identify_transfer_opportunities(transfer_system, source_domain, target_domain)

Identifies transfer opportunities from source to target domain.

meta_learn(transfer_system, adaptation_task, few_shot_examples)

Executes meta-learning for rapid adaptation to new tasks.

new(object_id, opts \\ [])

Creates a new transfer learning system for an object.

perform_analogical_reasoning(transfer_system, source_structure, target_structure)

Performs analogical reasoning to find structural correspondences.

transfer_knowledge(transfer_system, source_object, target_object, transfer_method \\ :automatic)

Transfers knowledge from source object to target object.

update_object_embedding(transfer_system, object, new_experiences)

Updates object embeddings in the shared embedding space.

Types

abstraction_level()

@type abstraction_level() :: integer()

action_id()

@type action_id() :: String.t()

adaptation_strategy()

@type adaptation_strategy() :: map()

analogy_engine()

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

analogy_key()

@type analogy_key() :: term()

analogy_result()

@type analogy_result() :: map()

analogy_template()

@type analogy_template() :: map()

domain_id()

@type domain_id() :: String.t()

domain_mapping()

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

embedding_model()

@type embedding_model() :: atom()

embedding_space()

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

embedding_vector()

@type embedding_vector() :: [float()]

episode()

@type episode() :: map()

feature_id()

@type feature_id() :: String.t()

knowledge_base()

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

learning_episode()

@type learning_episode() :: map()

mapping_rule()

@type mapping_rule() :: map()

meta_learning_state()

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

object_id()

@type object_id() :: String.t()

procedure()

@type procedure() :: map()

similarity_metric()

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

t()

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

task_id()

@type task_id() :: String.t()

transfer_method()

@type transfer_method() :: atom()

transfer_record()

@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()
}

Functions

compute_object_similarity(transfer_system, source_object, target_object)

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

evaluate_transfer_effectiveness(transfer_system)

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

identify_transfer_opportunities(transfer_system, source_domain, target_domain)

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

meta_learn(transfer_system, adaptation_task, few_shot_examples)

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

new(object_id, opts \\ [])

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", ...}

perform_analogical_reasoning(transfer_system, source_structure, target_structure)

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

transfer_knowledge(transfer_system, source_object, target_object, transfer_method \\ :automatic)

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

update_object_embedding(transfer_system, object, new_experiences)

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