At the end of this course, the students; 1) Learn knowledge representation and rule-based systems. 2) Understand decision trees and ID3 algorithm. 3) Learn forward and backward chaining algortihms. 4) Gain skills about methods of solving problems related to artificial intelligence and expert systems. 5) Gain experience to apply expert systems. 6) Design an expert system.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
Knowledge representation. Rule-base systems. Knowledge acquisition. Decision trees. ID3 algorithm. The inference engine: forward chaining, backward chaining, and backward chaining algorithms. Inexact reasoning. Uncertainty models in expert systems. Theory of expert systems. Design a expert system. Validity of knowledge base. Fuzzy expert systems. Frame-based expert systems. Hybrid Expert Systems.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Knowledge representation.
2nd Week
Rule-base systems.
3rd Week
Knowledge acquisition
4th Week
Decision trees, ID3 algorithm.
5th Week
The inference engine: forward chaining, backward chaining, and backward chaining algorithms.
6th Week
Inexact reasoning
7th Week
Uncertainty models in expert systems.
8th Week
Mid-term
9th Week
Theory of expert systems
10th Week
Design a expert system
11th Week
Validity of knowledge base.
12th Week
Fuzzy expert systems.
13th Week
Frame-based expert systems.
14th Week
Hybrid Expert Systems.
RECOMENDED OR REQUIRED READING
1. Jackson P., Introduction To Expert Systems, 3/E, Addision Wesley, 1998 2. Hayes- Roth F., Waterman D.A., Lenat D.B, Building Expert Systems, ISBN: 0201106868, Addison-Wesley, 1983 3. Turban E., Sharda R., Delen D., Decision Support and Business Intellegence Systems, 9/E, ISBN:9780132453233, Pearson, 2011