Stephen McGlinchey


Stephen McGlinchey received the BSc (Hons) degree in Computing Science from the University of Paisley in 1996, and went on to do a PhD in Neural Networks, which was completed in 2000, also at Paisley. He now works as a lecturer at the University of Paisley, teaching computer games technology. He has published several research papers, mainly on neural networks and artificial intelligence for games. Recently, his research work has focussed on ant colony algorithms for path-finding in computer games, and automatic post-processing of motion capture data.

Publications

Biologically Inspired Artificial Intelligence for Computer Games
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 278 pages.
Computer games are often played by a human player against an artificial intelligence software entity. In order to truly respond in a human-like manner, the artificial...
Contemporary Video Game AI
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 11 pages.
This chapter provides a brief outline of the history of video game AI – and hence by extension an extremely brief outline of some of the key points in the history of video games...
An Introduction to Artificial Neural Networks
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 12 pages.
The design of the first computers were influenced by the power of the human brain and attempts to create artificial intelligence, yet modern day digital computers are very...
Supervised Learning with Artificial Neural Networks
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 17 pages.
In this chapter we will look at supervised learning in more detail, beginning with one of the simplest (and earliest) supervised neural learning algorithms – the Delta Rule. The...
Case Study: Supervised Neural Networks in Digital Games
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 7 pages.
In this short chapter we present a case study of the use of ANN in a video game type situation. The example is one of duelling robots, a problem which, as we will see, lends...
Unsupervised Learning in Artificial Neural Networks
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 43 pages.
With the artificial neural networks which we have met so far, we must have a training set on which we already have the answers to the questions which we are going to pose to the...
Fast Learning in Neural Networks
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 14 pages.
We noted in the previous chapters that, while the multilayer perceptron is capable of approximating any continuous function, it can suffer from excessively long training times....
Genetic Algorithms
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 16 pages.
The methods in this chapter were developed in response to the need for general purpose methods for solving complex optimisation problems. A classical problem addressed is the...
Beyond the GA: Extensions and Alternatives
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 18 pages.
The last two chapters introduced the standard GA, presented an example case study and explored some of the potential pitfalls in using evolutionary methods. This chapter focuses...
Evolving Solutions for Multiobjective Problems and Hierarchical AI
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 11 pages.
Multi-Objective Problems, MOP, are a class of problems for which different, competing, objectives are to be satisfied and for which there is generally no single best solution –...
Artificial Immune Systems
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 30 pages.
We now consider the problem of introducing more intelligence into the artificial intelligence’s responses in real-time strategy games (RTS). We discuss how the paradigm of...
Ant Colony Optimisation
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 22 pages.
Ants are truly amazing creatures. Most species of ant are virtually blind; some of which have no vision at all, yet despite this, they are able to explore and find their way...
Reinforcement Learning
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 25 pages.
Just as there are many different types of supervised and unsupervised learning, so there are many different types of reinforcement learning. Reinforcement learning is appropriate...
Adaptivity within Games
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 12 pages.
This book centres on biologically inspired machine learning algorithms for use in computer and video game technology. One of the important reasons for employing learning in...
Turing's Test and Believable AI
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey. © 2008. 17 pages.
It is very evident that current progress in developing realistic and believable game AI lags behind that in developing realistic graphical and physical models. For example, in...