April 3, 2008

Artificial Intelligence – Knowledge Representation – issues, predicate logic, rules

Artificial Intelligence – Knowledge Representation, Issues, Predicate Logic, Rules

This is part of the courseware on Artificial Intelligence, by R C Chakraborty, at JUET. It consists eight hours of lectures. The topics are :

Knowledge Representation (KR) – introduction, knowledge progression, model, category, typology map, relationship, mapping, representation, schemes, relational, inheritable, inferential, declarative, procedural, attributes, relationship, granularity.

KR using Logic – predicate logic, propositional logic, statements, variables, symbols, connective, truth value, contingencies, tautologies, contradictions, antecedent, consequent, argument, expressions, quantifiers, formula, representing “IsA” and “Instance” relationships.

KR using Rules – declarative, procedural, meta rules, procedural verses declarative knowledge & language, logic programming characteristics, statement, language, syntax & terminology, data components, simple & structured data objects, program components clause, predicate, sentence, subject, queries, programming paradigms, model, computation, imperative model, functional model, logic model, reasoning, forward and backward chaining, conflict resolution, and control knowledge.

For complete course lecture slides move on to Website URL :


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