Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Reasoning and Intelligent Systems
Code CSCK502
Coordinator Dr F Grasso
Computer Science
Year CATS Level Semester CATS Value
Session 2023-24 Level 7 FHEQ Whole Session 15


1. To provide students with a comprehensive understanding of the domain of reasoning and intelligent systems.

2. To enable students to evaluate modern techniques of artificial intelligence and reasoning in both the public and the private sector contexts.

3. To provide students with the knowledge and skills required to develop and deploy the tools and techniques of intelligent systems to solve real world problems.

Learning Outcomes

(M1) An ability to analyse and evaluate intelligent systems techniques.

(M2) A comprehensive understanding of the differences between intelligent system applications and conventional computer applications.

(M3) An ability to deploy critically appropriate software tools and skills for the design and implementation of intelligent systems.

(M4) An in depth understanding of the practical application of the principles of intelligent systems.

(M5) An ability to analyse intelligent system problems and formulate appropriate solutions.

(S1) Communication skills in electronic as well as written form.

(S2) Self-direction and originality in tackling and solving problems within the domain of Computer Science, and an ability to act autonomously in planning and implementing solutions in a professional manner.

(S3) Experience of working in development teams and the leadership of such teams.

(S4) Group working, respecting others, co-operating, negotiating/persuading, awareness of interdependence with others.



Week 1: Introduction to Intelligent systems
The history of intelligent systems and their importance in real life applications, the characteristics of intelligent systems that serve to segregate them from other systems.

Week 2: Rule-based Expert Systems
How knowledge can be learnt, expressed and represented in the form of production rules; the characteristics of expert systems; forward and backward chaining inference techniques.

Week 3: Reasoning under uncertainty
How to reason with probability; representing beliefs about propositions; Bayesian belief networks.

Week 4: Evolutionary Computation Algorithms
Evolutionary computation algorithms as an application of Artificial Intelligence with an emphasis on Genetic Algorithms (GAs), their structure and application.

Week 5: Fuzzy Expert Systems
The concept of fuzzy logic and its theoretical underpinning, fuzzy sets and rules, fuzzy inference techniques and the main steps in developing fuzzy expe rt systems.

Week 6: Inductive reasoning
Statistical and inductive generalisation, simple and enumerative induction, rule induction systems.

Week 7: Temporal and spatial reasoning
Temporal frameworks and calculi, constraint based temporal reasoning, scheduling and activity planning.

Week 8: Intelligent systems applications
The application of intelligent systems in the modern work place. The philosophy and ethics of AI.

Teaching and Learning Strategies

The mode of delivery is by online learning, facilitated by a Virtual Learning Environment (VLE). This mode of study enables students to pursue modules via home study while continuing in employment. Module delivery involves the establishment of a virtual classroom in which a relatively small group of students (usually 10-25) work under the direction of a faculty member. Module delivery proceeds via a series of eight one-week online sessions, each of which comprises an online lecture, supported by other eLearning activities, posted electronically to a public folder in the virtual classroom. The mode of learning includes a range of required and optional eLearning activities, including but not limited to: lecture casts, live seminars, self-assessment opportunities, and required and suggested further reading and try-for-yourself activities. Communication within the virtual classroom is asynchronous, preserving the requirement that students are able to pursue the module in their own time, within the weekly time-frame of each online session. An important element of the module provision is active learning through collaborative, cohort-based, learning using discussion fora where the students engage in assessed discussions facilitated by the faculty member responsible for the module. This in turn encourages both confidence and global citizenship (given the international nature of the online student body).

Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24


Timetable (if known)              
Private Study 86


EXAM Duration Timing
% of
Penalty for late
CONTINUOUS Duration Timing
% of
Penalty for late
Discussion Question 1: Participate actively in an online discussion to critically discuss experiences and opinions within the cohort relating to intelligent systems.    20       
Discussion Question 2: Participate actively in an online discussion concerning one of the intelligent systems topics covered within the module, demonstrating an understanding of the key issues and sho    20       
Programming: Individual software solution to an intelligent systems' problem resulting in a demonstrable software system and supporting analysis in the form of a brief report (500 words).  12    30       
Report: Intelligent systems group project resulting in a demonstrable system and a group report describing and analysing the system.    30       

Recommended Texts

Reading lists are managed at Click here to access the reading lists for this module.