Module Identifier COM6010  
Academic Year 2007/2008  
Co-ordinator Dr Mora J McCallum  
Semester Available all semesters  
Other staff Mr David J Smith  
Pre-Requisite CO21120 , Available only to students taking the Diploma/MSc in Computer Science scheme in Singapore.  
Course delivery Other   Contact Hours. 34 hours of contact time plus about 65 hours of self study, practicals and assessment.  
Assessment TypeAssessment Length/DetailsProportion
Semester Exam2 Hours  100%
Supplementary Exam Supplementary examination will take the same form, under the terms of the Department's policy.   
Further details  

Learning outcomes

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

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.


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
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
How can we build an intelligent robot? Sensing, Action and Cognition. The symbolic approach. Memory, representation and reasoning.

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

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

5. Expert Systems
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
How can robot brains be built? Artificial neural nets, pattern recognition and learning.

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

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

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

Reading Lists

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


This module is at CQFW Level 7