Pre-requisites: Data Structures and Algorithms
COURSE OBJECTIVES:
To introduce the foundations of artificial intelligence.
ESSENTIAL TOPICS TO BE COVERED:
COURSE DESCRIPTION:
Introduction to Artificial Intelligence, Basic elements of AI, history, applications and
classification of techniques used. Production Systems and Search: Definition and examples of Production Systems. State Space Search: graph theory, strategies (data driven, goal driven), techniques (depth first, breadth first, etc.). Heuristic Search: definitions, techniques: hill climbing etc. Knowledge Representation: Knowledge representation issues, Procedural Knowledge Representation vs. Declarative Knowledge, Reasoning. Facts, Representing Knowledge using Rules, Logic Programming. Common Sense and Statistical Reasoning: Nonmonotonic reasoning
and modal logic for nonmonotonic reasoning. How to deal with Agents and their Beliefs. Use of Certainty Factors in Rule-Based Systems. Associating probabilities to assertions in first-order logic. Bayesian Networks. Expert Systems: Components of expert systems, development methodology (selection of problems, knowledge engineering), types (rule based, model based, case based), knowledge representation (rules, semantic networks, frames), inference, forward chaining, backward chaining, production systems and rule based expert systems. goal driven problem reasoning, data driven reasoning. (same as TE outline)
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