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2010/1 Module Catalogue
Module Provider: Computing Short Name: CSM10
Level: M Module Co-ordinator: BROWNE A Dr (Computing)
Number of credits: 15 Number of ECTS credits: 7.5
Module Availability

Spring semester

Assessment Pattern

Unit(s) of Assessment
Weighting Towards Module Mark( %)

Coursework (group): develop a working expert system using the shell provided


Coursework (individual): description and critical analysis of the expert system developed in group coursework (written report)


Assessment (individual): in-class tests to ensure a good understanding of the terminology and reinforce theoretical aspects of knowledge representation on computers

Qualifying Condition(s) 

A weighted aggregate of 50% is required to pass the module


Module Overview

Students are challenged to arrive at their own understanding of ‘intelligent behaviour’ in humans, and to explore the different ways in which this can be replicated using computers. With this acquired knowledge the module then investigates the major paradigms for representing expert human knowledge on computers, and compares those which require human knowledge to be re-engineered (including rule-based systems) with those which are trained by humans and can mimic the working of the human brain (neural networks). The coursework requires the students to form into groups and build a working expert system using the open-source shell provided.



Module Aims

The aim of the module is to equip students with the skills and critical awareness of the concept of ‘intelligence’ and human cognitive processes, and how these can be replicated in ‘intelligent’ computer systems. This will include a comprehensive understanding of knowledge representation schema and inferencing techniques, and the application of this understanding to the development of a working knowledge-based system.

Learning Outcomes

The students who successfully complete the module will learn to:

• identify and show critical awareness of the cognitive basis of human problem solving and decision making

• apply their knowledge in an original way to select the most appropriate problem solving and knowledge representation paradigm    for a given task

• critically evaluate the expert knowledge required for solving a specialised problem

• work effectively and professionally in small groups, and manage a small but complex project

• apply the knowledge gained to construct and evaluate a rule-based advisory system
Module Content

The following topics will be covered in the module:

introduction to intelligence – both human and artificial (with a medical flavour)

cognitive processes including perception, attention, categorization and problem solving

• knowledge representation using semantic networks and production rules

case study: inside MYCIN - a classic expert system
managing uncertainty and incomplete information

• algorithms and strategies for managing production rule systems

how to design and build a knowledge-based system using database development tools (4D)

alternative paradigms including frames, case-based reasoning and neural networks

future directions including the impact of pervasive and ubiquitous computing

Methods of Teaching/Learning

30 contact hours in weeks 1-10, consisting of:

• 20 hours of lectures and tutorials

• 10 hours of practical sessions, discussion groups and in-class tests

The examination will be of 1.5 hours duration


Selected Texts/Journals

Essential reading:

Negnevitsky M: Artificial Intelligence - A Guide to Intelligent Systems (Addison-Wesley 2004 2nd edition)

Recommended reading:

Giarratano J and Riley G: Expert Systems - Principles and Programming

(PWS 1998 3rd edition)
Parkin AJ: Essential Cognitive Psychology (Psychology Press 2001)

Background reading and other resources:


Turban E and Aronson JE: Decision Support Systems and Intelligent Systems

(Prentice Hall 2000 6th edition)

Jackson P: Introduction to Expert Systems (Addison Wesley 1998 3rd edition)

Winston PH: Artificial Intelligence (Addison-Wesley 1993 3rd edition)

Callan R: The essence of neural networks (Prentice Hall 1999)

Gurney K: An introduction to neural networks (UCL Press 1997)

Johnson-Laird P: The Computer and the Mind (Fontana1993 2nd edition)

Hofstadter D: Gödel, Escher, Bach: An Eternal Golden Braid (Penguin 1979)
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