The Course on AI refers to the even semester (Jan–May) course, title: Artificial Intelligence, Code 07B61CI3-0-2, 4 Credits, Lectures-42 hours. This course offered to the students of 6th semester B.Tech course in the year, 2006, 2007, 2008, 2009 and 2010. The lecture slides, 565 numbers in pdf format, have gone through four updates. The course is at the Dept. of Computer Science & Engineering, Jaypee University of Engineering and Technology (JUET), Guna. This course is offered by Prof. RC Chakraborty, Visiting Professor at JUET.

Acknowledgments

In the preparation of the course material, any quote, paraphrase or summary, information, idea, text, data, table, figure or any other material which originally appeared in someone else’s work, I sincerely acknowledge them.

last updated on Jan. 10, 2010

B.Tech Course on Artificial Intelligence, 42 hrs Topic 11, Slides 565 in pdf format

Content

00

Course Content

01

Introduction to AI : Definitions, Goals of AI, AI Approaches, AI Techniques, Branches of AI, Applications of AI.

02

Problem Solving, Search and Control Strategies : General problem solving , Search and control strategies , Exhaustive searches , Heuristic search techniques, Constraint satisfaction problems (CSPs), models .

03

Knowledge Representation Issues, Predicate Logic, Rules : Knowledge representation, KR using predicate logic, KR using rules .

04

Reasoning System - Symbolic, Statistical : Reasoning , Symbolic reasoning, Statistical reasoning .

05

Game Playing : Overview, Mini-Max search procedure, Game playing with Mini-Max, Alpha-Beta pruning .

06

Learning : What is learning, Rote learning, Learning from example : Induction, Explanation Based Learning (EBL), Discovery, Clustering , Analogy, Neural net and genetic learning, Reinforcement learning .

07

Expert System : Introduction, Knowledge acquisition, Knowledge base, Working memory, Inference engine, Expert system shells, Explanation, Application of expert systems .

08

Fundamentals of Neural Networks : Introduction and research history, Model of artificial neuron, neural network Characteristics, Learning methods, Single-layer network system, Applications.

09

Fundamentals of Genetic Algorithms : Introduction, Encoding, Operators of genetic algorithm, Basic genetic algorithm .

10

Natural Language Processing : Introduction, Syntactic processing, Semantic and pragmatic analysis .

11

Common Sense : Introduction, Physical world, Common sense ontologies, Memory organization .

In the preparation of the course material, any quote, paraphrase or summary, information, idea, text, data, table, figure or any other material which originally appeared in someone else’s work, I sincerely acknowledge them.

1

"Neural Network, Fuzzy Logic, and Genetic Algorithms - Synthesis and Applications", by S. Rajasekaran and G.A. Vijayalaksmi Pai, (2005), Prentice Hall, Chapter 1-15, page 1-435.

2

"Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig, (2002), Prentice Hall, Chapter 1-27, page 1-1057.

3

"Computational Intelligence: A Logical Approach", by David Poole, Alan Mackworth, and Randy Goebel, (1998), Oxford University Press, Chapter 1-12, page 1-608.

4

"Artificial Intelligence: Structures and Strategies for Complex Problem Solving", by George F. Luger, (2002), Addison-Wesley, Chapter 1- 16, page 1-743.

5

"AI: A New Synthesis", by Nils J. Nilsson, (1998), Morgan Kaufmann Inc., Chapter 1-25, Page 1-493.

6

"Artificial Intelligence: Theory and Practice", by Thomas Dean, (1994), Addison-Wesley, Chapter 1-10, Page 1-650.

7

Related documents from open source, mainly internet. An exhaustive list is being prepared for inclusion at a later date.

Introduction to AI :
Definitions, Goals of AI, AI Approaches, AI Techniques, Branches of AI, Applications of AI.

Problem Solving, Search and Control Strategies :
General problem solving , Search and control strategies , Exhaustive searches , Heuristic search techniques, Constraint satisfaction problems (CSPs), models .

Knowledge Representation Issues, Predicate Logic, Rules :
Knowledge representation, KR using predicate logic, KR using rules .

Reasoning System - Symbolic, Statistical :
Reasoning , Symbolic reasoning, Statistical reasoning .

Game Playing :
Overview, Mini-Max search procedure, Game playing with Mini-Max, Alpha-Beta pruning .

Learning :
What is learning, Rote learning, Learning from example : Induction, Explanation Based Learning (EBL), Discovery, Clustering , Analogy, Neural net and genetic learning, Reinforcement learning .

Expert System :
Introduction, Knowledge acquisition, Knowledge base, Working memory, Inference engine, Expert system shells, Explanation, Application of expert systems .

Fundamentals of Neural Networks :
Introduction and research history, Model of artificial neuron, neural network Characteristics, Learning methods, Single-layer network system, Applications.

Fundamentals of Genetic Algorithms :
Introduction, Encoding, Operators of genetic algorithm, Basic genetic algorithm .

Natural Language Processing :
Introduction, Syntactic processing, Semantic and pragmatic analysis .

Common Sense :
Introduction, Physical world, Common sense ontologies, Memory organization .