Module Information

Module Identifier
Module Title
Representation and Reasoning for Intelligent Systems
Academic Year
Semester 1

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Assessment Report:  Analytic report on scientific paper(s) (3000 words limit), including presentation and discussion   40%
Semester Assessment Essay:  topic in Representation and Reasoning for Intelligent Systems (3000 words)  60%
Supplementary Assessment Will take a form as agreed by Departmental exam board  100%

Learning Outcomes

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

A good understanding of how intelligent systems can represent, reason and react effectively about the environment in which they exist.

Appreciation of the diversity of the existing techniques for knowledge representation and inference as well as their respective strengths and limitations.

Awareness of the current state-of-the-art in both symbolic and semi-symbolic approaches for reasoning and revision with formally represented domain knowledge.

The ability and interest in applying advanced representation and reasoning techniques in solving real-world problems.

The ability to search for, and critically evaluate literature relevant to their assignment topic, as demonstrated in their assignment report

Brief description

The module will present a variety of advanced topics relevant to the building of practical intelligent systems. In addition to standard knowledge representation and reasoning techniques, it will cover most recent developments as well. The module will introduce the keep concepts taken by individual approaches and discuss the pros and cons of them. Students will be required to carry out independent review of carefully selected research papers and to present their findings in class seminars.


1. Constraint based techniques: (4 hrs)
Ideas of constraint satisfaction problems and typical algorithms for constraint satisfaction, constraint propagation and other solution techniques.

2. Uncertainty handling techniques: (6 hrs)
Example theories and their utility of representing and reasoning with uncertain knowledge, including Bayesian nets, Dempster-Shafer theory, fuzzy logic and rough set theory.

3. Symbolic belief revision techniques: (4 hrs)
Techniques for an intelligent system to make hypotheses and explore their consequences, covering reason maintenance and assumption-based truth maintenance.

4. Qualitative reasoning: (4 hrs)
Basic approaches for qualitatively representing and reasoning about the structure and behaviour of domain systems, focussing on the constraint-centred modelling paradigm.

5. Model based reasoning: (4 hrs)
Methods and tools for developing systems that utilise explicit models of domain problems, analysing the general diagnostic engine and its extensions, and systems for failure mode and effects analysis.

6. Case based reasoning: (4 hrs)
Principles and basic techniques for exploiting knowledge of experienced cases, discussing important issues of case indexing, case retrieval and case adaptation in such systems.

7. Example applications (4 hrs) of the different techniques/approaches introduced.

Module Skills

Skills Type Skills details
Application of Number Inherent to subject
Communication Seminar
Improving own Learning and Performance Inherent to subject
Information Technology Inherent to subject
Personal Development and Career planning Encourages students to see roles in subject for career and personal development
Problem solving Inherent to subject
Research skills Essay
Subject Specific Skills Representation and Reasoning for Intelligent Systems.

Reading List

General Text
(1984.) Qualitative reasoning about physical systems /edited by Daniel G. Bobrow. North-Holland Primo search Kuipers, Benjamin. (1994.) Qualitative reasoning :modeling and simulation with incomplete knowledge /Benjamin Kuipers. MIT Press Primo search Negnevitsky, Michael. (2011.) Artificial intelligencea guide to intelligent systems / Michael Negnevitsky. 3rd ed. Addison Wesley Parsons, Simon. (2001.) Qualitative methods for reasoning under uncertainty /Simon Parsons. MIT Press Primo search Russell, Stuart J. (2010.) Artificial intelligence :a modern approach /Stuart J. Russell and Peter Norvig ; contributing writers, Ernest Davis ... [et al.]. 3rd ed. Prentice Hall ; Primo search
Essential Reading
David Barber (2012) Bayesian Reasoning and Machine Learning Cambridge University Press Shen, Qiang (2000) Practical Reasoning Methodologies Lecture notes. University of Edingburgh Primo search


This module is at CQFW Level 7