Chapter 6: Knowledge Representation & Reasoning

6.1 What is Knowledge Representation?

Knowledge representation is how an AI system stores and organizes information to enable reasoning and problem solving.

Key Aspects:

6.2 Propositional Logic

The simplest form of logical representation using propositions (statements that are true or false).

Operator Symbol Example
AND (Conjunction) ∧ P ∧ Q
OR (Disjunction) ∨ P ∨ Q
NOT (Negation) ¬ ¬P
IMPLIES → P → Q
EQUIVALENT ↔ P ↔ Q

6.3 First-Order Logic (FOL)

More expressive than propositional logic, allowing:

Example: ∀x (Bird(x) → CanFly(x))

6.4 Semantic Networks

Graph-based representation showing relationships between concepts.

            Animal
              ↑
            Bird → CanFly
              ↑
            Penguin → ¬CanFly

6.5 Rule-Based Systems

Knowledge represented as IF-THEN rules.

Example:
IF temperature > 100 THEN patient has fever

6.6 Reasoning Methods

Method Description
Forward Chaining Start with known facts, apply rules to reach goal
Backward Chaining Start with goal, work backwards to find supporting facts
Resolution Proof by contradiction in logical systems

6.7 Frequently Asked Exam Questions

  1. Compare propositional and first-order logic with examples.
  2. Convert English statements to FOL expressions.
  3. Explain forward vs backward chaining with medical diagnosis examples.
  4. Draw a semantic network for a given domain.
  5. What are the limitations of rule-based systems?