CHM6010 - ARTIFICIAL INTELLIGENCE

Module Identifier	CHM6010
Module Title	ARTIFICIAL INTELLIGENCE
Academic Year	2001/2002
Co-ordinator	Dr Myra Wilson
Semester	Semester 2
Other staff	Professor Mark Lee, Dr Myra Wilson
Pre-Requisite	Available only to students taking the Diploma/MSc in Computer Science scheme in Aberystwyth.
Co-Requisite	CH21120
Course delivery	Lecture	20 Lectures
	Practical	Up to 3 x 2hr
Assessment	Supplementary examination	Supplementary examination will take the same form, under the terms of the Department's policy.
	Exam	2 Hours	100%
Further details	http://www.aber.ac.uk/compsci/ModuleInfo/CHM6010

Brief description

This module concentrates on the technology and methodology of artificial intelligence (AI) practice. The aim is to show how practical knowledge-based systems can be implemented, developed and evaluated.

Aims

This course aims to give students a good understanding of a variety of AI systems, from expert systems through machine learning to adaptive computing. This will enable them to appreciate what current AI systems can and cannot do, and the circumstances in which their use is appropriate.

Learning outcomes

On completion of this module, students should be able to:

identify the main application areas in which artificial intelligence has been applied;
discuss some of the main controversies within and around artificial intelligence, particularly those concerning the nature of intelligence, and the limits of artificial intelligence;
explain the contributions of artificial intelligence to computing in general, and to industry and commerce, particularly in the area of expert systems;
describe and assess artificial intelligence techniques for a selected set of application topics;
make intelligent use of artificial intelligence software.

Syllabus

1. Introduction to AI - 1 Lecture
What is AI? Introduction to the range of applications of AI. Why do we need AI and how do we use it?

2. Intelligent Agents - 1 Lecture
How can we build an intelligent robot? Sensing, Action and Cognition. The symbolic approach. Memory, representation and reasoning.

3. Machine Vision - 3 Lectures
How can robots see? The nature of the vision task. Computer vision and image processing.

4. Knowledge - 2 lectures
How can robots think? Knowledge representation methods. Reasoning and inference.

5. Expert Systems - 3 Lectures
How can human expertise be automated? Example applications and commercial successes. How to build an expert system - system concepts and architectures. Rule-based systems: design, operation and worked examples. Knowledge bases and knowledge based systems.

6. Artificial Brains - 2 Lectures
How can robot brains be built? Artificial neural nets, pattern recognition and learning.

7. Search and reasoning - 4 Lectures
Why do we need search? Evaluation of search strategies. Un-informed search techniques. Informed search techniques.

8. Machine Learning - 2 Lectures
How can a computer program learn? Inductive learning. Structural methods. Genetic algorithms.

9. Learning application - 2 Lectures
Case-based reasoning. Data mining.

Reading Lists

Books
** Recommended Text
S.J. Russell and P. Norvig. (1995) AI: A Modern Approach. Prentice-Hall ISBN 0131038052
Alison Cawsey. (1998) The Essence of Artificial Intelligence. Essence of Computing Series. Prentice Hall ISBN 0135717795
** Consult For Futher Information
J. Giarratano and G. Riley. (1998) Expert Systems: Principles and programming. Boston PWS ISBN 0534950531
E. Rich and K. Knight. (1991) Artificial Intelligence. 2nd.. McGraw Hill ISBN 0070522634