Artificial Intelligence

(updated Jan. 2014)

 

Course Type: Elective

Credits: 04

 

Objectives and prerequisites:

This is an introductory course in Artificial Intelligence (AI) with emphasis on foundations, evolutionary history, core concepts and application areas. The objective is to make the students familiar with AI as a discipline and as an applied area. The Course will have four units. The first unit (Introduction) deals with AI definitions and approaches, its evolution as a discipline, overview of subject matter and philosophical issues. The unit 2 (Problem Solving) focuses on Problem Solving Techniques in AI, with the underlying notion that Problem Solving in AI can be viewed as state space search. Different blind and heuristic search methods are discussed, including adversarial search encountered in game playing. The unit 3 (Knowledge Representation and Reasoning) presents a detailed account of need and concepts of knowledge representation structures and associated reasoning mechanisms. The unit 4 (Selected Topics) covers discussion on selected topics such as expert systems, multi-agent systems and AI & natural language processing.

The course assumes that students have successfully completed a basic course in Data Structures & Algorithms and Computer Programming. 

 

Contents:

Introduction:  Evolution of AI as a discipline, Definitions and approaches, Subject matter of AI, Foundations of AI, Philosophical issues. 

Problem Solving: Problem solving as state space search, Production system, Control strategies and problem characteristics; Search techniques: Breadth First and Depth-first, Hill Climbing, Heuristics, Best-First Search, A* algorithm, Problem reduction and AO* algorithm, Constraints satisfaction, Means Ends Analysis, Adversarial Search, Metaheuristics.

Knowledge Representation and Reasoning: Syntactic and Semantic representations, Predicate and prepositional logic, Resolution, Unification, Deduction and theorem proving, Question answering, Overview of PROLOG; Forward versus backward reasoning, Matching, Indexing; Semantic Net, Frames, Conceptual Dependencies and Scripts.

Selected Topics: Expert systems, Intelligent agent and Multi-agent Systems, AI and natural language processing, Learning from Data.

 

TEXT AND REFERENCE BOOKS: 

·         E. Rich, K. Knight and S. B. Nair, Artificial Intelligence, Mc Graw Hill, 3rd Ed., 2009. [Book Website]

·         S. Russel and P. Norvig, Artificial Intelligence: A Modern Approach, Pearson Education, 3rd Ed., 2009. [Companion Website] [AI on the Web – a wonderful page by authors]

·         Nils J Nilsson, The Quest for Artificial Intelligence, Cambridge University Press, 2009. [Book webpage]

·         George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Pearson Education, 6th ed., 2009. [Book Website]

·         Peter Jackson, Introduction to Expert Systems, Pearson Education, 3rd ed., 1999.

 

Some Other Classic Books:

·         Patrick Henry Winston, Artificial Intelligence, Addison Wesley, 3rd ed., 1992.

·         E. Charniak and D. McDermott, Introduction to Artificial Intelligence, Pearson Education, 1985.

·         D.W. Patterson, Introduction to Artificial Intelligence and Expert Systems, PHI, 2004.

 

WEB RESOURCES: 

 

Students are encouraged to visit following links about representative portals/ journals/ SIGs reporting research work in AI:

 

SOME leading CONFERENES in AI:   

 

·         IJCAI

·         AAAI

·         A Detailed list by Microsoft Academic Search is HERE

 

Topic-wise Suggested readings:

UNIT1:

AI Definitions and Approaches, AI Subject Matter, Foundations of AI, Evolutionary History of AI.

·         Russel and Norvig, Chapter 1

·         Rich, Knight and Nair, Chapter 1

·         Wikipedia Article on AI

·         Encyclopaedia Britannica article on AI

·         A Tutorial on AI by E Reingold (Developed for Psychology students)

 

Turing Test, Seale’s Argument and Philosophical Issues.

·         Russel & Norvig, Chapters 26 and 27

·         A. M. Turing, Computing Machinery and Intelligence, Mind 49: 433-460.

·         John R. Searle’s, Mind, Brains and Programs, The Behavioural and Brain Sciences (1980) 3, 417-457 [Local Copy]

 

AI Hall of Fame.

 

UNIT2:

Problem solving as state space search, Search techniques, Heuristics, Constraints satisfaction, Means Ends Analysis.

·         Rich, Knight and Nair, Chapters 2 and 3

·         Russel and Norvig, Chapters 3, 4 and 6

·         Simon & Newell, Human Problem Solving: The State of the Theory in 1970 [Local Copy]

·         S Luke, Essentials of Metaheuristics

 

Adversarial Search, Game Playing.

·         Russel and Norvig, Chapter 5

·         Rich, Knight and Nair, Chapter 12

 

UNIT3:

Knowledge Representation, Predicate and Prepositional Logic, Resolution, Procedural vs Declarative Knowledge, Forward vs Backward Reasoning.

·         Rich, Knight and Nair, Chapters 4, 5 and 6

·         Russel and Norvig, Chapters 8 and 9

 

Weak and Strong Slot and Filler Structures, Semantic Networks, Frames, Scripts.

·         Rich, Knight and Nair, Chapters 9, 10 and 11

·         Russel and Norvig, Chapter 12

 

Overview of Prolog, Program Structure, Facts and Rules, Operators, Reasoning.

·         Rich, Knight and Nair, Chapter 25

 

UNIT4:

Expert systems, Intelligent agent and Multi-agent Systems, AI and natural language processing, Learning from Data.

·         Rich, Knight and Nair, Chapter 20 and 15

·         Russel and Norvig, Chapter 22 and 2

·         More Material as the course progresses

 

 

Slides and PDF copies of some reading material will be shared as the class progresses. More printed and online material, particularly related to the assignments, will also be suggested.

 

ASSESSMENT CRITERIA:

Seminar - 15%

Periodical test - 15%

Home and Lab Assignment - 10%

Mid Semester Examination - 20%

End Semester Examination - 40%

 

LAB and PRESENTATION ASSIGNMENTS

 

 

 

Queries and Feedback may be routed to vivek@cs.sau.ac.in