Name Office Office hour Mail id K.Senthil Kumar

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1 SRM UNIVERSITY FACULTY OF ENGINEERING AND TECHNOLOGY SCHOOL OF COMPUTING DEPARTMENT OF CSE COURSE PLAN Course Code : CS0302 Course Title : Artificial Intelligence & Expert systems Semester : VII Course Time : Jul-Dec 2011 Day Hour Timing , Location : Tech Park Faculty Details Name Office Office hour Mail id Tech park Mon -Fri K.Senthil Kumar senthiliitkgp@ktr.srmuniv.ac.in 7th Floor Required Text Books: 1.Elaine Rich, "Artificial Intelligence", 2nd Edition, McGraw Hill, Dan W.Patterson, "Introduction to AI and ES", Pearson Education, 2007 REFERENCE BOOKS 1.Peter Jackson, "Introduction to Expert Systems", 3rd Edition, Pearson Education, Stuart Russel, Peter Norvig "AI - A Modern Approach", 2nd Edition, Pearson Education 2007 ONLINE REFERENCES

2 Prerequisite : CS0202 Principles of Programming Languages Objectives Assessment Details 1.To study the concepts of Artificial Intelligence 2.Methods of solving problems using Artificial Intelligence 3.Introduce the concepts of Expert Systems and machine learning Cycle I : 10Marks Surprise I : 5 Marks Cycle II : 10Marks Surprise II : 5 Marks Model Exam : 20 Marks Schedule S.No. DATE TEST TOPICS DURATION 1 Cycle - I Unit I & II 2 periods 2 Cycle - II Unit III & IV 2 periods 3 Model Exam All 5 units 3 Hrs Outcomes Students who have successfully completed this course will have full understanding of the following concepts 1. Various Ideas in AI 2. Various Types of Expert systems Detailed Session Plan INTRODUCTION TO Al AND PRODUCTION SYSTEMS Introduction to Al - Problem formulation, Problem Definition - Production systems, Control strategies, Search strategies. Problem characteristics, Production system characteristics - Specialized production systems - Problem solving methods - Problem graphs, Matching, Indexing and Heuristic functions - Hill Climbing, Depth first and Breath first, Constraints satisfaction - Related algorithms, Measure of performance and analysis of search algorithms.

3 Sessi on No Topics to be covered Time (min) Ref Teaching Method ing Method Introduction to Al - Problem formulation, Problem Definition Production systems, Control strategies, / practical Search strategies. Problem characteristics, Production system characteristics Specialized production systems - Problem solving methods, / practical Problem graphs, Matching, Indexing and Heuristic functions Hill Climbing, Depth first and Breath first, / practical, 7 Constraints satisfaction, Related algorithms, Measure of performance / practical 8 and analysis of search algorithms. 50 1,3 BB Related algorithms, Measure of performance / practical 9 and analysis of search algorithms. 50 1,3 BB REPRESENTATION OF KNOWLEDGE Game playing - Knowledge representation, Knowledge representation using Predicate logic, Introduction to predicate calculus, Resolution, Use of predicate calculus, Knowledge representation using other logic - Structured representation of knowledge. 10 Game playing Discussion 11 Knowledge representation Illustration by examples Knowledge representation using Predicate logic Introduction to predicate calculus 14 Resolution, 15 Use of predicate calculus 16 Knowledge representation using other logic Problem solving, surprise test 17 Structured representation of knowledge. Problem solving, surprise test FUNDAMENTALS OF EXPERT SYSTEMS Basic plan generation systems - Strips - Advanced plan generation systems - K strips - D Comp. Expert systems - Architecture of expert systems, Roles of expert systems - Knowledge Acquisition - Meta knowledge, Heuristics. 18 Basic plan generation systems Strips Advanced plan generation systems K strips Surprise

4 22 23 D Comp. Expert systems Architecture of expert systems 24 Roles of expert systems Knowledge Acquisition Meta knowledge Heuristics. KNOWLEDGE INFERENCE Knowledge representation - Production based system, Frame based system. Inference - Backward chaining, Forward chaining, Rule value approach, Fuzzy reasoning - Certainty factors, Bayesian probability Knowledge representation Production based system Frame based system Inference - Backward chaining Forward chaining Rule value approach,, Fuzzy reasoning Certainty factors 36 Bayesian probability. MACHINE LEARNING Strategic explanations - Why, Why not and how explanations. Learning - Machine learning, adaptive learning. - Typical expert systems - MYCIN, PIP, INTERNIST, DART, XOON, Expert systems shells. 37 Strategic explanations 38 Why, Why not and how explanations 39 Learning 40 Machine learning 41 adaptive learning 42 Typical expert systems 43 MYCIN, PIP 44 INTERNIST, DART 45 DART, XOON, Expert systems shells. BB Black Board

Course Outline Department of Computing Science Faculty of Science. COMP 3710-3 Applied Artificial Intelligence (3,1,0) Fall 2015

Course Outline Department of Computing Science Faculty of Science COMP 710 - Applied Artificial Intelligence (,1,0) Fall 2015 Instructor: Office: Phone/Voice Mail: E-Mail: Course Description : Students