Module Identifier CS36310  
Academic Year 2003/2004  
Co-ordinator Dr Mark B Ratcliffe  
Semester Semester 2  
Other staff Professor Ross D King, Dr Yonghuai Liu  
Pre-Requisite CS26210  
Course delivery Seminars / Tutorials   Up to 2 workshop sessions  
  Practical   Up to 1 session  
  Lecture   20 lectures  
Assessment TypeAssessment Length/DetailsProportion
Semester Assessment Course Work: Two practical assignments  100%
Supplementary Exam Will take the same form, under the terms of the Department's policy  100%
Further details  

Learning outcomes

On successful completion of this module students should:

Brief description

This module concentrates on the technology and methodology of Artificial Intelligence practice, in particular, programming languages and software systems. The aim is to build on the concepts introduced in CS26210 and show how knowledge-based systems can be implemented, developed and evaluated.


This course will provide a good understanding of a variety of AI systems from machine learning to expert systems. This will enable students to gain an understanding of the current state of the art in applied AI systems.


1. Effective Diagnostic Expert Systems - 4 Lectures
Building on CS36210, these lectures will introduce how to build effective diagnostic systems for man made systems. It will therefore look at diagnostic trees, diagnostic test benches and issues associated with diagnostic systems. From these lectures the student should gain an understanding of the features of commercial expert systems tools and their strengths and limitations.

2. Case based reasoning - 4 Lectures
These lectures will provide the student with an understanding of the techniques which comprise a case-based reasoning (CBR) system. They should also gain an understanding of their strengths and weaknesses for diagnostic applications as well as when to apply and when not to apply CBR. The lectures will consider the basic structure of a CBR system and the use of a case base, representation and indexing of cases, case retrieval and adaptation and finally case repair.

3. Model-based Reasoning - 1 Lectures
In these lectures "Functional" model based reasoning will be introduced. How a functional model-based system can be constructed and the problems and issues associated with such a task will be considered. In addition a functional model-based system constructed for Engineers will be considered in detail. This case study will highlight the trade offs which had to be made between ease of use and flexibility, power, etc.

4. MBR for Failure Mode Effect Analysis - 1 Lectures
These lectures will look at how MBR can be used as the basis of a design analysis system which can be used to provide information necessary for the construction of diagnostic systems.

5. General data analysis - 5 lectures
Probability theory, survey of data analysis methods, overfitting, cross-validation, ensemble learning.

6. Neural Networks - 2 Lectures
These lectures will introduce the concepts behind Neural Networks and investigate some of the more important and useful neural network architectures. They will also aim to give an appreciation of the application of neural computing in industry.

7. Machine Induction - 2 Lectures
There are a large number of applications which exploit the approach to machine learning known as induction. In these lectures we will consider the application pf these methods.

8. Inductive Logic Programming - 1 Lecture
Inductive Logic Programming (ILP) is a new and exciting development in AI systems which has a wide range of applications. This lecture will introduce ILP and the issues associated with applying ILP to real world problems.

9. Data Mining - 1 Practical
Data mining has become one of the fastest growing application areas in AI. It combines various AI techniques such as neural networks and machine induction with statistical analysis and database techniques. A Data Mining tool will be considered during this practical.

Reading Lists

** Recommended Text
Janet L. Kolodner (1993) Case-based reasoning Morgan Kaufmann
G. Luger and W. Stubblefield (1989) Artificial Intelligence and the Design of Expert Systems Benjamin/Cummings
T.M. Mitchell (1998) Machine Learning McGraw Hill


This module is at CQFW Level 6