Module Identifier CS36110  
Academic Year 2001/2002  
Co-ordinator Dr Mark Ratcliffe  
Semester Semester 1  
Other staff Dr George Coghill, Dr Yonghuai Liu  
Pre-Requisite CS26210  
Course delivery Lecture   20 lectures  
Assessment Supplementary examination   Will take the same form, under the terms of the Department's policy.    
  Exam   2 Hours   100%  
Further details  

General description

This module builds on CS26210 and examines some of the key ideas in Artificial Intelligence. A small number of topics are studied in depth in order to give insight and understanding of the methods and issues involved in state-of-the-art developments.


The primary aim of this module is to give students a deeper understanding of some of the principle concepts of artificial intelligence, developed in the Artificial Intelligence Concepts module CS26210 . In concentrating on model-based reasoning (MBR) and machine learning, this module will make full use of the expertise and research strengths of members of the lecturing staff.

Learning outcomes

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


Model Based Reasoning

1. Introduction to Model-based reasoning and its applications - 1 lecture

2. Basic concepts in MBR - 3 lectures
Qualitative arithmetic, quantity spaces, model-types, ontologies.

3. Introduction to Qualitative Simulation - 2 lectures
Mycroft and QSIM.

4. Problems and Solutions - 1 lecture
Spurious behaviour generation and some spcific solutions.

5. Example and Revisions - 1 lecture

Machine Learning - 12 Lectures

Reading Lists

** Recommended Text
T. Mitchell. (1997) Machine Learning. McGraw Hill 0070428077
** Consult For Futher Information
B.J. Kuipers. (1994) Qualitative Reasoning: modelling and simulation with incomplete knowledge. MIT Press 026211190X
S. M. Weiss and C. A. Kulikowsky. (1991) Computer Systems That Learn. Morgan Kaufmann 1558600655