AT21 ARTIFICIAL INTELLIGENCE & NEURAL NETWORKS

1. Scope of AI 1 hour

1.1 General Issues and overview of AI

1.2 The AI problems

1.3 Characteristics of AI applications

I [1]

2. Problem Solving, Search and Control Strategies 6 hours

2.1 General Problem solving

2.2 Control strategies

2.2.1 Forward and backward chaining

2.3 Exhaustive Searches

2.3.1 Depth first and Breadth first search.

2.4 Heuristic Search Techniques

2.4.1 Hill climbing

2.4.2 Branch and Bound technique

2.4.3 Best first search & A* algorithm

2.5 Constraint Satisfaction problems.

I [2, 3]

3. Game playing 5 hours

3.1 AND/OR graphs

3.2 Problem reduction & AO* algorithm

3.3 Minimax search procedure

3.4 Alpha-Beta cutoffs

3.5 Additional Refinements

I [12]

4. Knowledge Representations 9 hours

4.1 First order predicate calculus

4.1.1 Skolemization

4.1.2 Resolution Principle & Unification

4.1.3 Inference Mechanisms

4.1.4 Horn's clauses

4.2 Semantic Networks

4.3 Frame Systems and Value Inheritance

4.4 Scripts

4.5 Conceptual Dependency

I [9, 10]; II [5, 6, 10]

5. AI Programming Languages (PROLOG) 12 hours

5.1 Introduction to PROLOG

5.1.1 General Syntax and Prolog Control Strategy

5.1.2 Recursive Programming

5.1.3 Lists

5.1.4 Iterative Programming

5.2 Advanced Prolog Concepts

5.2.1 Cut, Fail predicates

5.2.2 Binary Trees and Objects

5.2.3 Meta Level Programming and Meta interpreters

II [7-10]

6. Learning 9 hours

6.1 Concept of Learning

6.2 Learning by Induction, Anology

6.3 Example Based Learning

6.4 Neural Networks

6.4.1 Perceptrons

6.4.2 Multilayer Feedforward Networks

6.4.3 Back Propagation Algorithm

6.4.4 Hopfield Network

6.4.5 Neural Network Applications

I [17, 18]

7. Planning 4 hours

7.1 Overview - An Example Domain: The Blocks Word

7.2 Component of Planning Systems;

7.3 Goal Stack Planning (linear planning)

7.4 Non-linear Planning using goal sets

I [13]

8. Handling Uncertainty 5 hours

8.1 Probabilistic Reasoning and Uncertainty

8.1.1 Probability theory

8.1.2 Bayes theorem and Bayesian networks

8.2 Fuzzy Logic

I [8]

9. Expert Systems 6 hours

9.1 Need and justification for Expert systems

9.2 Application of Expert systems

9.3 Expert System Architecture

9.3.1 Rule Based Expert System (Production Systems)

9.3.2 Non Production Systems

9.4 Various Expert System Shells

9.5 Knowledge Acquisition

9.6 Case studies: MYCIN and R1

I [20]

10. Natural Language Processing 3 hours

10.1 Parsing techniques

10.2 Context-free grammar

10.3 Recursive Transitions Nets (RTN)

10.4 Augmented Transition Nets (ATN)

10.5 Definite Clause Grammar (Logic grammar)

I [15]; II [11]

Text Books

I. Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw-Hills, 1991.

II. Logic and Prolog Programming - Saroj Kaushik, New Age International Ltd, publisher, 2002.

Reference Books

1. Dan W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, Prentice all of India, 1992.

2. S Russell & P Norvig, “Artificial Intelligence: A Modern Approach”, Pearson Education, reprint 2003.

3. N J. Nilsson, “Artificial Intelligence: A New Approach”, Morgan Kaufmann, reprint 2000.

4. L. Sterling & E. Shapiro, “Art of Prolog, Advanced Programming Techniques, Prentice Hall of India, reprint 1996.