Reasoning Agent

Summary

The Reasoning Agent is a specialized cognitive sub-agent that provides advanced logical reasoning capabilities for augmentic development. It implements deductive, inductive, and abductive reasoning using LogicEngine and LLM-based reasoning.

Technical Explanation

The Reasoning Agent implements three types of logical reasoning:

Deductive Reasoning: General to specific (syllogistic reasoning)

Inductive Reasoning: Specific to general (pattern finding)

Abductive Reasoning: Best explanation (inference to best explanation)

Architecture

Type: cognitive_sub_agent

Location: agents/core/reasoning_agent.py

Parent Agent: MastermindAgent

Lifecycle: On-demand (created when needed)

Integration: Uses LogicEngine for logical inference

Core Capabilities

Deductive Reasoning: Derives specific conclusions from general premises

Inductive Reasoning: Generalizes patterns from specific observations

Abductive Reasoning: Finds best explanations for observations

Logical Inference: Uses LogicEngine for rule-based inference

Belief Integration: Stores reasoning results in BeliefSystem

Reasoning Methods

deductive_reasoning: Performs deductive reasoning (general → specific)

inductive_reasoning: Performs inductive reasoning (specific → general)

abductive_reasoning: Performs abductive reasoning (best explanation)

logical_inference: Uses LogicEngine for rule-based inference

Usage

from agents.core.reasoning_agent import ReasoningAgent
from agents.core.belief_system import BeliefSystem
from agents.memory_agent import MemoryAgent
from agents.orchestration.coordinator_agent import CoordinatorAgent

Create reasoning agent
reasoning_agent = ReasoningAgent(
    agent_id="reasoning_agent",
    belief_system=belief_system,
    coordinator_agent=coordinator_agent,
    memory_agent=memory_agent
)

Initialize async components
await reasoning_agent._async_init()

Deductive reasoning
result = await reasoning_agent.deductive_reasoning(
    premises=[
        "All agents are intelligent",
        "BDI agent is an agent"
    ],
    conclusion_hint="What can we conclude about BDI agent?"
)

Inductive reasoning
result = await reasoning_agent.inductive_reasoning(
    observations=[
        "Agent A succeeded with approach X",
        "Agent B succeeded with approach X",
        "Agent C succeeded with approach X"
    ],
    pattern_hint="What pattern emerges?"
)

Abductive reasoning
result = await reasoning_agent.abductive_reasoning(
    observations=[
        "System performance decreased",
        "Memory usage increased",
        "CPU usage increased"
    ],
    possible_explanations=[
        "Memory leak",
        "Resource contention",
        "Inefficient algorithm"
    ]
)

Integration with MastermindAgent

The Reasoning Agent is created on-demand by MastermindAgent for augmentic development tasks:

# MastermindAgent creates reasoning agent
result = await mastermind_agent._create_sub_agent(
    agent_type="reasoning_agent",
    agent_id="reasoning_001",
    config={}
)

Integration with LogicEngine

The Reasoning Agent uses LogicEngine for rule-based logical inference:

# LogicEngine provides:
- Rule-based inference
- Forward chaining
- Safe expression evaluation
- Socratic questioning support

NFT Metadata (iNFT/dNFT Ready)

iNFT (Intelligent NFT) Metadata

{
  "name": "mindX Reasoning Agent",
  "description": "Specialized cognitive sub-agent for advanced logical reasoning (deductive, inductive, abductive)",
  "image": "ipfs://[avatar_cid]",
  "external_url": "https://mindx.internal/agents/core/reasoning_agent",
  "attributes": [
    {
      "trait_type": "Agent Type",
      "value": "cognitive_sub_agent"
    },
    {
      "trait_type": "Capability",
      "value": "Logical Reasoning"
    },
    {
      "trait_type": "Reasoning Types",
      "value": "Deductive, Inductive, Abductive"
    },
    {
      "trait_type": "Lifecycle",
      "value": "on_demand"
    }
  ]
}

Design Decisions

Three Reasoning Types: Supports deductive, inductive, and abductive reasoning

LogicEngine Integration: Uses LogicEngine for rule-based inference

LLM-Based: Uses LLM for complex reasoning tasks

Belief Integration: Stores results in BeliefSystem for persistence

On-Demand: Created when needed, not always-on

Future Enhancements

Probabilistic reasoning

Temporal reasoning

Causal reasoning

Multi-agent reasoning

Reasoning chain validation

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