Object.MetaDSL (object v0.1.2)

Self-Reflective Meta-DSL implementation based on AAOS specification.

Provides primitives and constructs for objects to reason about and modify their own learning process, enabling "learning to learn" capabilities.

Core constructs include:

  • DEFINE: Define new attributes, methods, or sub-objects
  • GOAL: Query or modify the object's goal function
  • BELIEF: Update or query the object's beliefs about the environment
  • INFER: Perform probabilistic inference using the world model
  • DECIDE: Make decisions based on current state and goals
  • LEARN: Update learning parameters or strategies
  • REFINE: Meta-learning to improve learning efficiency

Summary

Types

modification_record()
t()
trigger()

Functions

belief(meta_dsl, object, operation)

BELIEF construct: Update or query the object's beliefs about the environment.

decide(meta_dsl, object, decision_context)

DECIDE construct: Make decisions based on current state and goals.

define(meta_dsl, object, definition)

DEFINE construct: Defines new attributes, methods, or sub-objects.

evaluate_adaptation_triggers(meta_dsl, object, performance_metrics)

Evaluates adaptation triggers and executes automatic adaptations.

execute(meta_dsl, construct, object, args)

Executes a meta-DSL construct with the given arguments.

goal(meta_dsl, object, operation)

GOAL construct: Query or modify the object's goal function.

infer(meta_dsl, object, inference_query)

INFER construct: Perform probabilistic inference using the world model.

learn(meta_dsl, object, learning_operation)

LEARN construct: Update learning parameters or strategies.

new(opts \\ [])

Creates a new Meta-DSL instance with default constructs and parameters.

refine(meta_dsl, object, refinement_target)

REFINE construct: Meta-learning to improve learning efficiency.

Types

modification_record()

@type modification_record() :: %{
  timestamp: DateTime.t(),
  construct: atom(),
  modification: any(),
  success: boolean(),
  impact_score: float()
}

t()

@type t() :: %Object.MetaDSL{
  adaptation_triggers: [trigger()],
  constructs: [atom()],
  execution_context: map(),
  learning_parameters: map(),
  meta_knowledge_base: map(),
  self_modification_history: [modification_record()]
}

trigger()

@type trigger() :: %{
  condition: function(),
  action: atom(),
  threshold: float(),
  active: boolean()
}

Functions

belief(meta_dsl, object, operation)

BELIEF construct: Update or query the object's beliefs about the environment.

Parameters

  • meta_dsl: Meta-DSL system struct
  • object: Object to operate on
  • operation: :query, {:update, key, value}, or {:uncertainty, key, uncertainty}

Returns

{:ok, result, updated_meta_dsl} with beliefs or updated object

decide(meta_dsl, object, decision_context)

DECIDE construct: Make decisions based on current state and goals.

Parameters

  • meta_dsl - Meta-DSL system struct
  • object - Object making the decision
  • decision_context - Decision context:
    • {:action_selection, available_actions} - Choose optimal action
    • {:resource_allocation, resources, tasks} - Allocate resources
    • {:coalition_formation, potential_partners} - Form coalitions

Returns

{:ok, decision_result, updated_meta_dsl} with decision outcome

define(meta_dsl, object, definition)

DEFINE construct: Defines new attributes, methods, or sub-objects.

Parameters

  • meta_dsl: Meta-DSL system struct
  • object: Object to modify
  • definition: Definition tuple like {:attribute, name, value} or {:method, name, impl}

Returns

{:ok, updated_object, updated_meta_dsl} on success

evaluate_adaptation_triggers(meta_dsl, object, performance_metrics)

Evaluates adaptation triggers and executes automatic adaptations.

Parameters

  • meta_dsl: Meta-DSL system struct
  • object: Object to evaluate triggers for
  • performance_metrics: Current performance data

Returns

{:ok, updated_object, updated_meta_dsl} with any triggered adaptations applied

execute(meta_dsl, construct, object, args)

Executes a meta-DSL construct with the given arguments.

Parameters

  • meta_dsl: Meta-DSL system struct
  • construct: Construct to execute (:define, :goal, :belief, :infer, :decide, :learn, :refine)
  • object: Object to apply construct to
  • args: Arguments for the construct

Returns

{:ok, result, updated_meta_dsl} on success, {:error, reason} on failure

goal(meta_dsl, object, operation)

GOAL construct: Query or modify the object's goal function.

Parameters

  • meta_dsl: Meta-DSL system struct
  • object: Object to operate on
  • operation: :query, {:modify, new_goal}, or {:compose, goal_functions}

Returns

{:ok, result, updated_meta_dsl} where result depends on operation

infer(meta_dsl, object, inference_query)

INFER construct: Perform probabilistic inference using the world model.

Parameters

  • meta_dsl - Meta-DSL system struct
  • object - Object to perform inference on
  • inference_query - Query specification:
    • {:bayesian_update, evidence} - Bayesian belief update
    • {:predict, state, horizon} - State prediction
    • {:causal, cause, effect} - Causal inference

Returns

{:ok, inference_result, updated_meta_dsl} with inference results

learn(meta_dsl, object, learning_operation)

LEARN construct: Update learning parameters or strategies.

Parameters

  • meta_dsl - Meta-DSL system struct
  • object - Object updating learning
  • learning_operation - Learning operation:
    • {:update_parameters, new_params} - Update learning parameters
    • {:adapt_strategy, performance_feedback} - Adapt learning strategy
    • {:transfer_knowledge, source_domain, target_domain} - Transfer knowledge

Returns

{:ok, learning_result, updated_meta_dsl} with learning updates

new(opts \\ [])

Creates a new Meta-DSL instance with default constructs and parameters.

Parameters

  • opts: Optional configuration with :constructs, :execution_context, :learning_parameters

Returns

New Meta-DSL struct with initialized constructs

Examples

iex> Object.MetaDSL.new()
%Object.MetaDSL{constructs: [:define, :goal, :belief, :infer, ...], ...}

refine(meta_dsl, object, refinement_target)

REFINE construct: Meta-learning to improve learning efficiency.

Parameters

  • meta_dsl - Meta-DSL system struct
  • object - Object being refined
  • refinement_target - Target for refinement:
    • :exploration_strategy - Improve exploration approach
    • :reward_function - Refine reward function
    • :world_model - Improve world model accuracy
    • :meta_parameters - Adjust meta-learning parameters

Returns

{:ok, refinement_result, updated_meta_dsl} with refinement updates