This module builds on the ideas of MAT201 to comprise a more advanced study of mathematical methods and models relevant to Computer Games Technology, and introduces some of the many Artificial Intelligence (AI) techniques which are currently, or could in the near future, be used to enhance the development of applications in video games, or other entertainment related products. These AI techniques can enhance the immersive properties of a game by enabling ‘realistic’ and ‘believable’ game play and character actions, or used to reduce development time by automatically creating content, etc.
The aim of this module is to provide the student with: an appreciation of the advanced mathematical methods required in the study of Computer Games Technology and introduce the underlying techniques used in video games to create the illusion of ‘intelligence’ as well as some real AI techniques which are, or could be, used to enhance the development of these Game AI methods.
By the end of this module the student should be able to:
1. Model and solve more advanced problems in rigid body and 2-dimensional particle dynamics.
2. Use numerical methods to solve equations.
3. Develop a critical understanding of AI techniques and technologies.
4. Evaluate the use of AI technologies and techniques in computer games.
1 Numerical Methods:
Numerical methods for integration (trapezium and Simpson’s rules) and the solution of equations by simple iteration and the Newton-Raphson method. Numerical solution of DE’s, e.g. Euler, predictor/corrector methods (Euler/trapezium/Simpson), Verlet, Runge-Kutta.
2 Motion of a Rigid Body:
Centroids and moments of inertia of simple bodies, parallel and perpendicular axis theorems. Rotation of a rigid body about an axis, energy, angular momentum. Rolling and sliding motion.
3 An Introduction to AI for Games:
The importance of good game AI. The differences between Game AI and so called ‘real’ Academic AI and their relative advantages and disadvantages.
4 ‘Traditional’ Game AI:
Rule Based Systems, Finite State Machines.
5 Academic AI Techniques:
Fuzzy Logic and Fuzzy State Machines, Case Based Reasoning, Genetic Algorithms, Reinforcement Learning, Probabalistic Techniques, Artificial Neural Networks, Clustering Algorithms.
6 The use of AI in Games:
Combining AI techniques to produce A-life and Intelligent Agents. The future of AI in games.
Teaching and Learning Work Loads
|Teaching and Learning Method||Hours|
SCQF Level - The Scottish Credit and Qualifications Framework provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.
Credit Value – The total value of SCQF credits for the module. 20 credits are the equivalent of 10 ECTS credits. A full-time student should normally register for 60 SCQF credits per semester.
We make every effort to ensure that the information on our website is accurate but it is possible that some changes may occur prior to the academic year of entry. The modules listed in this catalogue are offered subject to availability during academic year 2021/22 , and may be subject to change for future years.