|| CHM6010 |
|| ARTIFICIAL INTELLIGENCE |
|| 2003/2004 |
|| Dr Edel M Sherratt |
|| Semester 2 |
|| Professor Mark H Lee, Dr Myra S Wilson |
|| Available only to students taking the Diploma/MSc in Computer Science scheme in Aberystwyth. |
|| CH21120 |
| Course delivery
|| Lecture || 20 Lectures |
|| Practical || Up to 3 x 2hr |
|Assessment Type||Assessment Length/Details||Proportion|
|Semester Exam||2 Hours ||100%|
|Supplementary Exam|| Supplementary examination will take the same form, under the terms of the Department's policy. || |
|| http://www.aber.ac.uk/compsci/ModuleInfo/CHM6010 |
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.
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.
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.
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 - 2 Lectures
How can we build an intelligent robot? Sensing, Action and Cognition.
The symbolic approach and alternatives. Computers and Brains.
3. Machine Vision - 3 Lectures
How can robots see? The nature of the vision task.
Computer vision and image processing.
4. Search - How can computers find solutions? 5 lectures
Why do we need search? Evaluation of search strategies.
Un-informed search techniques. Informed search techniques.
5. Machine Learning - How can a computer program learn? 3 lectures
Game playing. Genetic algorithms. Decision tree learning.
6. Knowledge - 2 lectures
How can robots think? Knowledge representation methods.
Reasoning and inference.
7. Expert Systems - 2 Lectures
How can human expertise be automated? Example applications and commercial
How to build a rule-based expert system, - operation and worked examples.
8. Artificial Brains - 2 Lectures
How can robot brains be built? Artificial neural nets, pattern recognition
** 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
This module is at CQFW Level 7