Computer Science, Prifysgol Cymru Aberystwyth University of Wales
C363(h)* - Artificial Intelligence Systems
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
C362(h)
and show how knowledge-based systems can
be implemented, developed and evaluated.
Aims, Objectives, Syllabus, Booklist
Further Details
- Number of lectures
- 12
- Number of seminars/tutorials
- 6
- Number of practicals
- 6 x 1-hour
- Coordinator
- Dr. Fred Long
- Other staff involved
- Mr. Patrick Olivier
- Pre-requisites
-
C362(h)
- Co-requisites
- None
- Incompatibilities
- None
- Assessment
- Assessed coursework - 20%
Written exam - 80%
- Timing
- This half module is offered only in Term 2
Aims
This module addresses the issues concerning the development
of knowledge-based systems. This necessarily involves consideration
of:
-
the characteristics of the problem domain;
-
knowledge aquisition methods;
-
evaluation of a suitable knowledge engineering
toolkit;
-
software engineering methodology for knowledge-based
systems;
It is assumed that students undertaking this module
will spend considerable time outside of lectures following of course
of directed reading, consisting of technical reports, technology
surveys and case studies. The module seminars will be a forum for
discussing issues arising from this wider reading, and their
relatationship to issues discussed in the lectures.
Objectives
On successful completion of this module students should:
-
be aware of the implications of a problem domain for the
selection of knowledge aquisition methodology, knowledge engineering
toolkit, software development methodology, and system evaluation
strategy;
-
understand the differences between the main classes of
knowledge engineering toolkit, including cost-benefit considerations,
representation and reasoning facilitites, and performance
issues;
-
understand the different techniques and methodologies
available for knowledge aquisition, and their relative strengths and
weaknesses;
-
appreciate the problem of software engineering for
knowledge-based systems;
-
have had practical exposure to a range of knowledge
engineering toolkits: including an expert system shell; a loosely
coupled toolkit; and a high-end intergrated toolkit;
Syllabus
-
Overview of AI Systems - 1 Lecture
-
Reasons for building knowledge-based systems.
Overview of issues in knowledge-based system development. Survey of
typical knowledge-based systems.
-
Current Expert System
Technology - 2 Lectures, 3 Practicals
-
Overview of rule-based expert systems. Introduction
to a commercial expert system shell. Representation, inference,
control flow, and interface issues. A first attempt at constructing
an expert system. Rule-based expert system shell
programming.
-
Classifying the Problem
Domain - 1 Lecture
-
Classification problems: monitoring, prediction,
diagnosis and decision classification. Construction problems: design,
planning, simulation and repair. Composite problems: process control,
troubleshooting and instruction. Case studies.
seminar.
-
Knowledge Engineering Toolkits - 4 Lectures, 3 Practicals
-
Classifying of knowledge engineering toolkits.
Artificial Intelligence languages, loosely coupled toolkits,
integrated toolkits, high-end integrated toolkits, expert system
shells. Case studies. Implications of the problem domain
classification for toolkit selection.
-
Knowledge
Acquisition - 2 Lectures
-
Knowledge acquisition and knowledge elicitation.
Problems with knowledge elicitation. Stages in knowledge acquisition.
Goal decomposition techniques. Classification techniques. Automating
the knowledge acquistion process.
-
The Knowledge Engineering
Lifecycle - 2 Lectures
-
Waterfall and spiral models. Feasibility study;
requirements specification; system design; system construction;
testing. Knowledge-based system for software engineering. One seminar
on the role of software engineering in AI.
Booklist
Students are likely to need ready access to the following
-
C. Price.
Knowledge Engineering Toolkits.
Ellis Horwood, 1989.
-
P. Harmon and B. Sawyer.
Creating Expert Systems for Business and Industry.
Wiley, 1990.
-
Pedersen.
Expert Systems Programming.
Wiley, 1989.
-
M. Bramer.
Practical Experience in Building Expert Systems.
Wiley, 1990.
-
E. C. Payne and R. Macarthur.
Developing Expert Systems: a knowledge engineer's handbook for
rules and objects.
Wiley USA, 1990.
-
G. Luger and W. Stubblefield.
Artificial Intelligence and the Design of Expert Systems.
Benjamin/Cummings, 1989.
Version 2.1
Syllabus
Nigel Hardy Departmental Advisor
nwh@aber.ac.uk
Dept of Computer Science, UW Aberystwyth (disclaimer)