8EE2 ARTIFICIAL INTELLIGENCE TECHNIQUES
 

1. INTRODUCTION TO AI: Definition, Applications. Components of an AI program; production system, problem characteristics. Overview of searching techniques. Knowledge representation Knowledge representation issues, an overview. Representing knowledge using rules, procedural varsus declarative knowledge. Logic programming Forward versus backward reasoning, MATCHING control knowledge.

2. STATISTICAL REASONING: Introduction to probability and Baye's theorem, certainty factor and rule based systems.

3. ARTIFICIAL NEURAL NETWORKS: Biological Neuron, Neural Net, use of neural nets, applications. Perception, idea of single layer and multiplayer neural nets, back propagation, Hopfield nets, suppervised and unsupervised learning.

4. EXPERT SYSTEMS: Basic idea of expert system. Expert system building tools and shells. Components of expert systems.

RECOMMENDED BOOKS:

1. Elaine Rich and Kevin Knight, Artificial Intelligence, TMH Pub.

2. James A Anderson, An introduction to Neural Networks.

3. Dan. W Patterson, Artificial Intelligence and Expert Systems.