Reasoning Engines

A reasoning engine is the component of a cognitive architecture that manipulates knowledge to derive new conclusions, solve problems, or plan actions.

Types of Reasoning

1. Deductive Reasoning (Top-Down)

Applying general rules to specific cases.

• Logic: If $P \to Q$ and $P$ is true, then $Q$ must be true.
• Strengths: Truth-preserving, verifiable.

2. Inductive Reasoning (Bottom-Up)

Generalizing from specific observations to general rules.

• Pattern Matching: I've seen 100 white swans, so all swans are likely white.
• Strengths: Discovering new knowledge from data.

3. Abductive Reasoning (Explanation)

Finding the most likely explanation for an observation.

• Inference: The ground is wet $\to$ it probably rained.
• Strengths: Essential for diagnosis and causal understanding.

The AGI Reasoner Pipeline

graph LR
    KB[Knowledge Base] -->|Input| Reasoner[Reasoning Engine]
    Goal[Goal/Query] -->|Input| Reasoner
    
    subgraph Reasoner Internals
    Search[Search/Heuristics]
    Rules[Inference Rules]
    Unification[Pattern Matching]
    end
    
    Reasoner -->|Output| NewFact[New Fact / Action]
    NewFact -->|Feedback| KB

Neuro-Symbolic Inference

Modern AGI reasoning combines the "feeling" of neural networks with the "logic" of symbolic engines.

• Neural Layer: Provides intuitive, fast suggestions for which rules to apply (heuristics).
• Symbolic Layer: Uses those rules to perform precise, verifiable steps.

Key Reasoners in AGI

• PLN (Probabilistic Logic Networks): Used in Hyperon to handle uncertainty.
• SAT Solvers: Used for hard logical constraints.
• Differentiable Logic: Neural networks that can perform logical operations.

Next: Learning Modules