Knowledge Representation
For AI to reason, it needs to store and organize knowledge. Knowledge representation is the field of AI concerned with how to represent information in a form that a computer can use to solve complex problems.
Knowledge representation is about translating real‑world knowledge into a format that AI can reason with.
Why Is It Important?
Without knowledge representation, AI would be like a calculator without numbers. It cannot make inferences or decisions. Good representation allows AI to:
- Draw conclusions (inference)
- Handle incomplete information
- Explain its reasoning
Common Techniques
- Logical representation: Uses formal logic (propositional, first‑order). Example:
∀x (Human(x) → Mortal(x)) - Semantic networks: Graph of nodes (concepts) and edges (relationships). Example: Bird → (is‑a) → Animal.
- Frames: Objects with slots for attributes. Example: Car {color: red, wheels: 4}.
- Production rules: IF‑THEN rules. Example: IF (temperature > 100) THEN (alarm = on).
Example: Family Relationships
Using logic:
Mother(Alice, Bob) ∧ Parent(x,y) → Child(y,x)From these, AI can deduce that Bob is a child of Alice. This is inference.
Challenges
- Real‑world knowledge is messy, incomplete, and sometimes contradictory.
- Trade‑off between expressiveness and computational efficiency.
Two Minute Drill
- Knowledge representation structures information so AI can reason.
- Techniques: logic, semantic networks, frames, production rules.
- Inference draws new conclusions from existing knowledge.
- Challenges: messy real‑world knowledge and efficiency.
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