Inference

The process of deriving new knowledge from existing knowledge. The core of logical agents. Can be computationally expensive.

If $\alpha$ can be inferred from KB using a sound inference algorithm, it is denoted as: $$

Sound

When an inference algorithm never derives false conclusions.

Complete

When an inference algorithm can derive all valid conclusions.

Monotonicity

When an inference algorithm’s results do not invalidate previously entailed conclusions.

Rules

Logical transformation rules used for inferencing.

• Modus Ponens:
  From $\alpha ⇒ β$ and $\alpha$, infer $β$.
• And-Elimination:
  From $(α ∧ β)$, infer $α$ (or $β$).
• Biconditional Elimination:
  From $(α ⇔ β)$, infer $(α ⇒ β)$ and $(β ⇒ α)$.

Techniques

Model Checking

Enumerate all models to verify whether $\alpha$ is true in all models that satisfy KB.

Theorem Proving

Applies inference rules directly to derive proofs instead of checking every model.

Scales better for large knowledge bases. Produces symbolic proofs instead of exhaustive enumeration.

Proof by Resolution

A rule-based inference method suitable for automated theorem proving. Allows fully mechanical proofs and is widely used in logic-based AI systems. Applies to sentences in Conjunctive Normal Form.

Resolution rules combines complementary literals to simplify until either no other resolution is possible.