Module Information

Module Identifier
Module Title
Artificial Intelligence
Academic Year
Semester 2
Other Staff

Course Delivery

Delivery Type Delivery length / details
Lecture 28 x 1 Hour Lectures


Assessment Type Assessment length / details Proportion
Semester Assessment Written Assignment:  30 hours  40%
Semester Exam 2 Hours   Written Exam  60%
Supplementary Assessment Resubmission:  Resubmission of failed/nonsubmitted coursework components or ones of equivalent value.  40%
Supplementary Exam 2 Hours   Resit Exam  60%

Learning Outcomes

Write simple programs in Java to search for solutions using AI techniques discussed in the module.

Demonstrate an understanding of the learning methods discussed in the module.

Describe the importance of propositional and predicate logic in Artificial Intelligence systems.

Solve simple problems in propositional and first order predicate logic.

Explain the function and use of fuzzy logic.

Brief description

This module begins with a motivational section on the use of AI in computer games programming: one of the cutting-edge applications of AI in use today. It then focuses on the foundational issue of search (finding solutions to problems and games), naturally followed by evolutionary algorithms, optimisation and swarms. A key concept of knowledge represention is introduced, followed by a variety for representional techniques from logic and fuzzy logic. The course finishes with the use of AI techniques for data mining.


1. Introduction to AI – 1 lecture
What is required to get computers to produce games AI?
Using AI and agents as a principle for this module.
Applications of AI such as bioinformatics, business, robotics.

2. Search – 6 lectures
Depth- and breadth-first search. Usng minimax as an example of simplified game playing. Greedy/best-first search A*search.

3. Optimisation with Evolutionary Algoritms and Swarms – 5 lectures
Evolutionary algorithms, evolutionary loop, selection mechanisms, variation operators. Particle swarm optimisation

4. Knowledge representation and logic – 5 lectures
Symbolic and sub-symbolic representations, logical representation for problems solving, inference deduction, abduction, induction

5. Fuzzy Logic – 5 lectures
Fuzzy sets and rules.
Fuzzy systems.

6. Data Mining, clustering and classification- 4 lectures
K-means clustering.
Hierarchical clustering.
Overview of other clustering methods
Introducing the idea of learning from data.

7. AI in research – 1 lecture

8. Revision – 1 lecture

Module Skills

Skills Type Skills details
Application of Number Many AI techniques require number application
Communication Written skills needed for the written assessment.
Improving own Learning and Performance Written assignment requires self-motivated study and work.
Information Technology Inherent in the topic.
Personal Development and Career planning Will feed into students' future career plans.
Problem solving Written assignment promote and assess this.
Research skills Assessing AI techniques for use in the written assignments requires reading and researching other materials.
Subject Specific Skills AI techniques
Team work None.


This module is at CQFW Level 5