Adaptivity within Games
Darryl Charles (University of Ulster, Ireland), Colin Fyfe (University of Paisley, UK), Daniel Livingstone (University of Paisley, UK) and Stephen McGlinchey (University of Paisley, UK)
Copyright: © 2008
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 computer games is that there is a strong desire by many developers and publishers within the industry to make games adaptive. For example, Manslow (2002) states, ‘The widespread adoption of learning in games will be one of the most important advances ever to be made in game AI. Genuinely adaptive AIs will change the way in which games are played by forcing each player to continually search for new strategies to defeat the AI, rather than perfecting a single technique.’ However, the majority of learning techniques to date that have been used in commercial games have employed an offline learning process, that is, the algorithms are trained during the development process and not during the gameplay sessions after the release of the game. Online learning—that is, learning processes that occur during actual gameplay—has been used in only a handful of commercial games, for example, Black and White, but the use of learning online within games is intrinsically linked to adaptivity and the use of the algorithms in this way needs to be explored more fully.