1. Module Description Template
This description is drawn up in a standard format. It is designed to describe the level of the module,
what the student learns to do by undertaking it and how that performance is assessed.
The template is laid out in boxed format, which you may choose to follow if you wish. Please type
within the cells of the table to facilitate the maintenance of the standard format which you may choose
to follow. Add or delete rows if necessary but it should not be necessary to add columns. Please fill
in and then delete all italic text [guidance notes] before you print it. In completing this module
description, arial font and font size 12 is recommended for purposes of accessibility.
MODULE TITLE: Introduction to Knowledge Based Systems
MODULE CODE: COM340J2
DATE OF REVISION: 2006/2007
MODULE LEVEL: 2
CREDIT POINTS: 20
MODULE STATUS: Optional]
SEMESTER: 2
LOCATION: Jordanstown.
E-LEARNING: web supplemented
PREREQUISITE(S): None
CO-REQUISITE(S): None.
MODULE CO-ORDINATOR(S): Patterson, WRD
TEACHING STAFF RESPONSIBLE Patterson, WRD
FOR MODULE DELIVERY:
HOURS: Indicate total notional student effort hours and their division
between lectures, seminars, tutorials, practicals, private
study, etc (10 hours = 1 credit point)
Lectures 24
Seminars 0
Tutorials 12
Practicals 24
Independent study 140
(including assessment)
TOTAL EFFORT HOURS: 200
ACADEMIC SUBJECT: Computing
MODULAR SUBJECT:

2. RATIONALE
Knowledge-based systems are widely used in business and industry, where they have provided
solutions to many complex problems and brought about significant productivity gains.
Successful application of the technology requires an understanding of:- the underlying principles
of knowledge representation; the different reasoning paradigms available and the ability to
ascertain which is most suitable to a particular problem; the skills in a language suitable for KBS
implementation. This module provides an introduction to the principles and practice of
knowledge-based systems, with appropriate emphasis on programming and implementation
issues.
AIMS
To develop an appreciation of the types of problem-solving applications for which knowledge-
based systems are useful.
To develop an understanding of the principles of knowledge representation and reasoning.
To develop an appreciation for how the field of knowledge based systems has evolved and
matured to meet new challenges
To develop an understanding of the knowledge acquisition process
To develop skills in the use of a programming language suitable for the implementation of
knowledge-based systems.
To develop knowledge engineering skills.
LEARNING OUTCOMES
Learning Outcomes should be statements of the minimum that a student will be able to do when they
have completed the module.
Learning outcomes should:
• be written in the future tense;
• identify important learning requirements;
• be achievable and assessable; and
• use language that students can understand.
Further advice is available from the University of Ulster Assessment Handbook.
Adapt the following table to suit your needs. The categories are those used in the Programme
Specification. Not all categories need be addressed in each module and the number of learning
outcomes is not fixed. Learning outcomes should be compatible with the level descriptors. See
Appendix 8 of the Programme Approval, Management and Review handbook for further information.
Follow a numbering scheme since these will be referred to in the Assessment section and will assist
in drawing up a programme specification.
You may wish to identify learning outcomes that are not essential (i.e. above the minimum) but which
nevertheless add value. These need not be assessed. Do not number them.
A successful student will be able to show that he/she can:

3. KNOWLEDGE AND UNDERSTANDING
K1 Demonstrate an understanding of different knowledge representation techniques such
as semantic networks, decision trees, logic, frames and object orientated approaches,
case-based reasoning and hybrid systems.
K2 Construct a KBS using an appropriate tool.
K3
K4
INTELLECTUAL QUALITIES
I1 Specify and design a KBS that employs inferencing
I2
I3
I4
PROFESSIONAL/PRACTICAL SKILLS
P1 Work as an integral member of a team to develop a KBS
P2
P3
P4
TRANSFERABLE SKILLS
T1 Communicate key concepts of their work to their peers
T2
T3
T4

4. CONTENT
Introduction
Introduction to artificial intelligence and knowledge-based systems.
Problems solving as search.
Role of expertise in problem solving
Knowledge-based systems and their applications
Historical development and distinctive features of knowledge-based systems.
Knowledge-based system architectures.
User interface design issues: explanation of reasoning, mixed- initiative dialogue,
sensitivity analysis.
Tools for building knowledge-based systems.
Applications in business, industry and medicine.
Advances in knowledge-based systems technology.
Knowledge-based systems and their applications
Historical development and distinctive features of knowledge-based systems.
Knowledge-based system architectures.
User interface design issues: explanation of reasoning, mixed- initiative dialogue,
sensitivity analysis.
Tools for building knowledge-based systems.
Applications in business, industry and medicine.
Advances in knowledge-based systems technology.
Knowledge representation and reasoning strategies
Propositional logic and predicate calculus.
Reasoning with semantic networks.
Frames and object-oriented representations.
Production rules: forward chaining, backward chaining, mixed inference strategies,
conflict resolution.
Decision tree representation of knowledge.
Meta-knowledge and explicit control of reasoning.
Reasoning in the presence of uncertainty.
Case-based reasoning as a problem solving methodology, its processes, knowledge
containers and applications
Deductive reasoning and model-based reasoning.
Knowledge-based systems development
Assessing the appropriateness of knowledge-based systems development.
Constructive vs classification problem-solving; generic problem-solving methods.
Knowledge acquisition: the knowledge engineering bottleneck, source of knowledge,
knowledge elicitation techniques process, machine learning and its role in knowledge
acquisition.
Knowledge base validation and maintenance.

5. TEACHING AND LEARNING METHODS
Lectures will provide students with the history and
current practice on the current topics of Knowledge
Based Systems.
Tutorials will offer the student the opportunities to
complete example examination questions and to query
the lecturers on any problem areas.
Practical exercises will introduce students to “hands
on” learning designed to reinforce the theory of the
previous weeks lectures. It will include experience of
the design and development, of a Hybrid KBS
combining both rules, and objects.
Students will be directed to a reading at appropriate points in the course
The module is web supplemented

6. ASSESSMENT
Course work 1: 20% of overall coursework mark
The first assignment is designed to ensure that students
understand the processes of forward and backward chaining
essential to the operation of any rule based KBS. They are
provided with a set of rules and a number of known facts.
They have to demonstrate how a conclusion is reached
from the facts using a) forward chaining and b) backward
chaining.
This assignment will measure the student's achievement of
learning outcome (iii) for the module COM340J2
Course work 2: 80% of overall coursework mark
The second assessment is designed to ensure that the
students can design and develop a prototype KBS using
rules, frames and objects. They must choose a problem
domain and elicit relevant knowledge from an identified
source of expertise. They must then build a system which
can reason within the domain. They work in groups of 5 for
this assignment and are required to present their system as
part of an oral presentation to the class. They are assessed
based on their presentation and a written report submitted
to the lecturer.
This assignment will measure the student's achievement of
learning outcomes (iv) for the module COM340J2
Examination:
The examination is three hours in length consisting of six
questions and is closed book. The students are required to
answer four questions. It is a closed book exam.
The examination will measure the student's achievement of
learning outcomes (i), (ii), (iii), (iv), (v) and (vi) for the
module COM340J2
Give the distribution of marks between coursework and examination
25 % Coursework 75 % Examination
READING LIST
List all required and indicative recommended reading. These should include electronic sources. Use the
Harvard referencing system throughout: Author, (Year), Title, Place of Publication, Publisher
Required Artificial Intelligence: A Guide to Intelligent Systems
(2nd Edition) by Michael Negnevitsky. Addison
Wesley. 2004
Recommended Expert Systems Design and Development J. Durkin
(006.3/DUR)

7. Artificial Intelligence, Structures and Strategies for
Complex Problem Solving. G. Luger & W.
Stubblefield (006.3/LUG)
SUMMARY DESCRIPTION
This module provides an introduction to knowledge-based systems and their applications in
business, industry and medicine. Topics covered include the principles of knowledge
representation and reasoning, Knowledge-based system architectures, explanation of
reasoning, management of uncertainty, roles of meta-knowledge, generic problem-solving
methods and knowledge elicitation. Appropriate emphasis is placed on programming and
implementation issues.
Academic Office
January 2006