MSc Dissertation: Games that learn about their users and adapt
September 9, 2010
This dissertation is about describing those techniques that can be used to develop a computer player that has the ability to adapt to its opponent and its environment in realtime, by learning about them. For this purpose, there has been a lot of research done, mostly academic, in fields such as expert systems, machine learning and others.
I choose to investigate the usefulness of some Machine Learning and Case Base Reasoning techniques because I felt that it consists of some very promising methods. I did that by presenting their general usability and possible applications in games and more particular in Real Time Strategy games.
I choose to research the applicability of such algorithms in an online (realtime) unsupervised environment as this is a requirement of the initial research subject.
Furthermore, this dissertation has as a goal to present one of the algorithms that are under the scope of this research in a practical manner. I did this by using an open-source software game engine and applying our my implementation of the algorithm.
The algorithm and general concept was inspired by the work Pieter Spronck, especially this paper.
