Artificial Intelligence

Course Title: Artificial Intelligence

Course no: 304                                                                                Full marks: 60+20+20

Credit hour: 3                                                                                  Pass marks: 24+8+8

Course contents:

Unit 1: Introduction to artificial intelligence

Artificial intelligence and related fields, brief  history of AI, applications of artificial intelligence, definition and importance of knowledge and learning.

Unit 2: Problem solving

Problem definition, problem as a state space search, problem formulation, problem types, well-defined problems, constraint satisfaction problem, game playing, production systems.

Unit 3: Search Techniques

Uniformed search techniques- depth first search, breadth first search, depth limit search, and search strategy comparison, Informed search techniques-hill climbing, best first search, greedy search, A* search, adversarial search techniques- minimax procedure, alpha beta procedure.

Unit 4: Knowledge representation, inference and reasoning

Formal logic-connectives, truth tables, syntax, semantics, tautology, validity, well-formed-formula, propositional logic, predicate logic, FOPL, interpretation, qualification, horn clauses, rules of inference, unification, reasoning refutation system (RRS), answer extraction from RRS, rule based deduction system, statistical reasoning- probability and Bayes’ theorem and causal networks, reasoning in belief network.

Unit 5: Structured knowledge representation

Representation and Mappings, approaches to knowledge representation, issues in knowledge representation, semantic nets, frames, conceptual dependencies and scripts.

Unit 6: Machine learning

Concepts of learning, learning from examples, explanation based learning, learning by analogy, learning by simulation evolution, learning by training neural nets, learning by training perceptions.

Unit 7: Application of artificial intelligence

Expert systems, neural networks, natural language processing, machine vision.

Text book:

1.       E. Rich and Knight, Artificial intelligence, McGraw Hill.

2.       D.W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall.

3.       P. H. Winston, Artificial intelligence, Addison Wesley.

4.       Stua Russel and Peter Nervig, Artificial Intelligence A Modern Approach, Pearson

5.       Ivan Bratko, PROLOG Programming for Artificial Intelligence, Addison Wesley.