Knowledge Representation and Reasoning in AI For students.

Knowledge representation stores what an AI knows in computer-usable form, and reasoning derives new conclusions from it. Instead of raw data like "temp = 38", a knowledge base holds meaning: facts such as "Ravi has a fever" and rules such as "IF fever AND cough THEN possibly flu", letting the system infer "Ravi may have the flu" by itself.

Intelligent behavior needs usable knowledge, not just fast math. A medical assistant must combine symptoms with medical rules; a troubleshooting tool must combine error codes with repair knowledge. Good representation keeps knowledge correct and extendable; good reasoning keeps answers reliable and explainable.

Key Concepts

• Fact: a single true statement, such as "Socrates is human".
• Rule: an IF-THEN statement, such as "IF X is human THEN X is mortal".
• Ontology: a formal vocabulary of a domain's concepts and how they relate.
• Inference engine: the program that applies rules to facts to derive new facts.
• Forward chaining: starts from facts and applies rules repeatedly toward the goal.
• Backward chaining: starts from the goal and works back to supporting facts.
• Semantic network: knowledge drawn as a graph of concepts and labeled relations.

How It Works

1. Expert knowledge is encoded as facts and IF-THEN rules in the knowledge base.
2. The user supplies the current case as facts (symptoms, readings, answers).
3. The inference engine fires the rules whose conditions match the facts.
4. New conclusions become facts that trigger further rules, until nothing new appears.
5. The system reports the conclusion plus the chain of rules used — a full explanation.

Worked Example

Knowledge base: 3 facts (fur = yes, milk = yes, meat = yes); R1: IF milk THEN mammal; R2: IF mammal AND meat THEN carnivore. Round 1: R1 matches and fires, adding "mammal" — facts go from 3 to 4. Round 2: R2 matches and fires, adding "carnivore" — facts go from 4 to 5. Round 3: nothing new matches, so reasoning stops. Totals: 2 rules fired, 2 facts derived, 3 rounds. Some systems also combine certainty: if two rules support one conclusion with certainty factors 0.8 and 0.6, the combined certainty is 0.8 + 0.6 times (1 − 0.8) = 0.8 + 0.12 = 0.92.

Common Misconceptions

• A knowledge base is just a database. Databases store facts; knowledge bases add rules and an inference engine that derives unstated knowledge.
• More rules always means smarter. Contradictions and slow reasoning follow; quality beats quantity.
• AI reasoning is like human intuition. Rule-based reasoning is formal and brittle outside its written coverage.

Real-World Applications

• Medical diagnosis assistants: in the tradition of the 1970s MYCIN system.
• Troubleshooting wizards: chaining symptoms to fixes for printers, networks, and cars.
• Loan and insurance approval: written policy rules applied to applicant facts.
• Semantic web: schema.org ontologies help search engines understand pages.

Representation gives AI a structured memory of facts and rules; chaining turns it into explainable conclusions. Where knowledge is explicit and answers must be justified rather than guessed, this approach still wins.