Object.NeuroSymbolicReasoning (object v0.1.2)

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

Summary

Types

attention_weights()
cognitive_state()
proof_step()
reasoning_trace()
state()
symbolic_expression()
tensor()

Functions

causal_inference(query, evidence)

Performs causal inference and counterfactual reasoning.

child_spec(init_arg)

Returns a specification to start this module under a supervisor.

explain_reasoning(trace)

Generates explanations for reasoning decisions.

few_shot_learn(examples, query)

Performs few-shot learning with minimal examples.

learn_from_experience(trace, feedback)

Learns from experience and updates neural and symbolic components.

meta_reflect()

Meta-cognitive self-reflection and strategy adaptation.

reason(input, context \\ %{})

Performs multi-modal reasoning on complex input.

start_link(opts \\ [])

Starts the neuro-symbolic reasoning engine.

Types

attention_weights()

@type attention_weights() :: %{
  query: tensor(),
  key: tensor(),
  value: tensor(),
  output: tensor()
}

cognitive_state()

@type cognitive_state() :: %{
  working_memory: [symbolic_expression()],
  episodic_memory: %{required(term()) => reasoning_trace()},
  semantic_knowledge: %{required(binary()) => symbolic_expression()},
  neural_parameters: %{required(binary()) => tensor()},
  meta_cognition: %{
    self_model: symbolic_expression(),
    uncertainty_estimates: %{required(term()) => float()},
    reasoning_strategies: [binary()]
  }
}

proof_step()

@type proof_step() :: %{
  rule: binary(),
  premises: [symbolic_expression()],
  conclusion: symbolic_expression(),
  justification: binary(),
  confidence: float()
}

reasoning_trace()

@type reasoning_trace() :: %{
  input: term(),
  neural_activations: %{required(non_neg_integer()) => tensor()},
  symbolic_derivations: [proof_step()],
  attention_patterns: %{required(non_neg_integer()) => attention_weights()},
  final_conclusion: term(),
  confidence: float(),
  explanation: binary()
}

state()

@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()
}

symbolic_expression()

@type symbolic_expression() :: %{
  type: :atom | :variable | :compound | :quantified,
  functor: binary() | nil,
  args: [symbolic_expression()],
  variables: [binary()],
  constraints: [symbolic_expression()]
}

tensor()

@type tensor() :: %{
  data: [float()],
  shape: [non_neg_integer()],
  dtype: :float32 | :float64 | :int32 | :int64
}

Functions

causal_inference(query, evidence)

@spec causal_inference(symbolic_expression(), [symbolic_expression()]) ::
  {:ok, [symbolic_expression()]} | {:error, term()}

Performs causal inference and counterfactual reasoning.

child_spec(init_arg)

Returns a specification to start this module under a supervisor.

See Supervisor.

explain_reasoning(trace)

@spec explain_reasoning(reasoning_trace()) :: {:ok, binary()} | {:error, term()}

Generates explanations for reasoning decisions.

few_shot_learn(examples, query)

@spec few_shot_learn([{term(), term()}], term()) :: {:ok, term()} | {:error, term()}

Performs few-shot learning with minimal examples.

learn_from_experience(trace, feedback)

@spec learn_from_experience(reasoning_trace(), term()) :: :ok | {:error, term()}

Learns from experience and updates neural and symbolic components.

meta_reflect()

@spec meta_reflect() :: {:ok, map()} | {:error, term()}

Meta-cognitive self-reflection and strategy adaptation.

reason(input, context \\ %{})

@spec reason(term(), map()) :: {:ok, reasoning_trace()} | {:error, term()}

Performs multi-modal reasoning on complex input.

start_link(opts \\ [])

Starts the neuro-symbolic reasoning engine.