MTRA-203	EXPERT SYSTEMS
Lecture	Tutorial	Practical	Credit	Major Test	Minor Test	Total	Time
4	0	0	4	60	40	100	3 Hrs.
Program Objective (PO)	The course gives students knowledge and skills for solving medium to hard problems from diverse expert systems application domains.
Course Outcomes (CO)
CO1	Define and describe expert system and its main constituents.Distinguish class of problems suitable for solving with expert systems.
CO2	Assemble various parts of knowledge and skills in order to devise the approach to solution.
CO3	Design and create expert system suitable for solving particular problem.
CO4	Appraise the quality of solution and justify the employed techniques

### Unit 1

The meaning of an expert system, problem domain and knowledge domain, the advantages of an expert system, general stages in the development of an expert system, general characteristics of an expert system, history and uses of expert systems today, rule-based expert systems, procedural and nonprocedural paradigms, characteristics of artificial neural systems. -The study of logic, difference between formal logic and informal logic, meaning of knowledge, how knowledge can be represented, semantic nets, how to translate semantic nets into PROLOG, limitations of semantic nets, schemas, frames and their limitations, how to use logic and set symbols to represent knowledge, the meaning of propositional and first order predicate logic, quantifiers, imitations of propositional and predicate logic.

### Unit 2

Trees, lattices, and graphs, state and problem spaces, AND-OR trees and goals, methods of inference, rules of inference, limitations of propositional logic, logic systems, resolution rule of inference, resolution systems, and deduction, shallow and causal reasoning, applying resolution to first-order predicate logic, forward and backward chaining, additional methods of reference, Meta knowledge, the Markov decision process.

### Unit 3

The meaning of uncertainty and theories devised to deal with it, types of errors attributed to uncertainty, errors associate, with induction, features of classical probability, experimental and subjective probabilities, compound and conditional probabilities, hypothetical reasoning and backward induction, temporal reasoning, Markov chains, odds of belief, sufficiency and necessity, role of uncertainty in inference chains, implications of combining evidence, role of inference nets in expert systems, how probabilities are propagated.

### Unit 4

Sources of uncertainty in rules, methods of dealing with uncertainty, Dempster-Shafer theory, theory of uncertainty based on fuzzylogic, commercial applications of fuzzy logic. How to select an appropriate problem, the stages in the development of an expert system, types of errors to expect in the development stages, the role of the knowledge engineer in the building of expert systems, the expected life cycle of an expert system, how to do a life cycle model.

##### Text and Reference Books :

- 1. J. Giarratano and G. Riley, "Expert Systems -- Principles and Programming". 4th Edition, PWS Publishing Company, 2004.
- 2. Durkin, J., Expert systems Design and Development, Macmillan, 1994 2. Elias M. Awad, Building Expert Systems, West Publishing Company 1996
- 3. Peter Jackson, Introduction to Expert Systems, Addison Wesley Longman, 1999.ISBN 0- 20187686-8.
- 4. Gonzalez and D. Dankel, "The Engineering of Knowledge-Based Systems'', Prentice Hall, 1994.
- 5. Nikolopoulos, "Expert Systems", Marcel Dekker Inc. 1997. ISBN 0 8247 9927 5

Note for paper setter for paper setter : Eight question will be set in all. Two questions from each unit. The candidate will be required to attempt four question in all and selecting one question from each unit.