Object.AIReasoning (object v0.1.2)

Advanced AI reasoning capabilities for AAOS objects using DSPy framework. Provides pre-built signatures for common object behaviors and interactions.

Summary

Functions

adapt_behavior(object_id, current_behavior, metrics, environment, goals)

Adapts object behavior based on performance feedback and environmental changes.

analyze_message(object_id, sender, content, context)

Analyzes incoming messages using AI reasoning to determine intent, priority, and recommended actions.

get_reasoning_performance(object_id)

Gets performance metrics for the reasoning system.

initialize_object_reasoning(object_id)

Initializes AI reasoning capabilities for an object by starting a DSPy bridge and registering common reasoning signatures.

plan_interaction(object_id, targets, goal, resources, constraints)

Plans optimal interaction strategies with other objects or agents.

register_custom_signature(object_id, name, signature_spec)

Registers a custom DSPy signature for specific reasoning tasks.

solve_problem(object_id, problem, information, constraints, criteria)

Performs systematic problem-solving using chain-of-thought reasoning.

synthesize_learning(object_id, experiences, knowledge, feedback, context)

Synthesizes learning from experiences and updates knowledge base.

Functions

adapt_behavior(object_id, current_behavior, metrics, environment, goals)

Adapts object behavior based on performance feedback and environmental changes.

Parameters

  • object_id: The ID of the reasoning object
  • current_behavior: Description of current behavior patterns
  • metrics: Recent performance data and feedback
  • environment: Current environmental conditions
  • goals: Object's current goals and objectives

Returns

Behavior adaptation recommendations with reasoning and risk assessment

analyze_message(object_id, sender, content, context)

Analyzes incoming messages using AI reasoning to determine intent, priority, and recommended actions.

Parameters

  • object_id: The ID of the reasoning object
  • sender: ID of the message sender
  • content: Message content to analyze
  • context: Current object state and interaction history

Returns

AI analysis result containing intent, priority, suggested actions, and confidence score

get_reasoning_performance(object_id)

Gets performance metrics for the reasoning system.

Parameters

  • object_id: The ID of the reasoning object

Returns

Performance metrics including query count, cache hits, and average latency

initialize_object_reasoning(object_id)

Initializes AI reasoning capabilities for an object by starting a DSPy bridge and registering common reasoning signatures.

Parameters

  • object_id: The ID of the object to initialize reasoning for

Returns

  • {:ok, object_id} on successful initialization
  • {:error, reason} if initialization fails

Examples

iex> Object.AIReasoning.initialize_object_reasoning("agent_1")
{:ok, "agent_1"}

plan_interaction(object_id, targets, goal, resources, constraints)

Plans optimal interaction strategies with other objects or agents.

Parameters

  • object_id: The ID of the reasoning object
  • targets: List of objects/agents to interact with
  • goal: Desired outcome of the interaction
  • resources: Resources available for the interaction
  • constraints: Any limitations or constraints to consider

Returns

Interaction plan with strategy, timing, and fallback options

register_custom_signature(object_id, name, signature_spec)

Registers a custom DSPy signature for specific reasoning tasks.

Parameters

  • object_id: The ID of the reasoning object
  • name: Name for the custom signature
  • signature_spec: Specification of inputs, outputs, and instructions

Returns

:ok on successful registration

solve_problem(object_id, problem, information, constraints, criteria)

Performs systematic problem-solving using chain-of-thought reasoning.

Parameters

  • object_id: The ID of the reasoning object
  • problem: Clear description of the problem to solve
  • information: All relevant information and data
  • constraints: Limitations and requirements to consider
  • criteria: How to measure successful resolution

Returns

Problem analysis, solution approach, implementation plan, and verification method

synthesize_learning(object_id, experiences, knowledge, feedback, context)

Synthesizes learning from experiences and updates knowledge base.

Parameters

  • object_id: The ID of the reasoning object
  • experiences: Recent experiences and outcomes
  • knowledge: Current knowledge and beliefs
  • feedback: External feedback received
  • context: Environmental and situational context

Returns

Key insights, knowledge updates, pattern recognition, and future applications