| Module Identifier |
CS16010 |
| Module Title |
AN INTRODUCTION TO ARTIFICIAL INTELLIGENCE |
| Academic Year |
2003/2004 |
| Co-ordinator |
Dr Mark B Ratcliffe |
| Semester |
Semester 2 |
| Other staff |
Professor Mark H Lee, Dr Myra S Wilson |
| Course delivery |
Lecture | 20 lectures |
| |
Practical | 3 x 2 hr |
| Assessment |
| Assessment Type | Assessment Length/Details | Proportion |
|---|
| Semester Exam | 2 Hours Online Examination | 100% |
| Supplementary Exam | Will take the same form, under the terms of the Department's policy | |
|
| Further details |
http://www.aber.ac.uk/compsci/ModuleInfo/CS16010 |
Learning outcomes
On completion of this module, students should be able to:
-
describe a range of standard artificial intelligence techniques including search, knowledge representation, machine vision, expert systems and machine learning;
-
appreciate the strengths and weaknesses of different artificial intelligence technologies for real world applications;
-
describe some of the contributions of artificial intelligence to computing in general, and to industry and commerce, particularly in the area of expert systems.
Brief description
The module will provide students with an introduction to the problems and techniques of artificial intelligence.
Aims
To introduce students to the problems and techniques of artificial intelligence.
Content
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
successes.
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
and learning.
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
Notes
This module is at CQFW Level 4