Hallam, John

Levine, John

Yannakakis, Georgios N.

2006-03-03T16:21:21Z

2006-03-03T16:21:21Z

2005-12

Institute of Perception, Action and Behaviour

Which features of a computer game contribute to the player’s enjoyment of it? How can we automatically generate interesting and satisfying playing experiences for a given game? These are the two key questions addressed in this dissertation. Player satisfaction in computer games depends on a variety of factors; here the focus is on the contribution of the behaviour and strategy of game opponents in predator/prey games. A quantitative metric of the ‘interestingness’ of opponent behaviours is defined based on qualitative considerations of what is enjoyable in such games, and a mathematical formulation grounded in observable data is derived. Using this metric, neural-network opponent controllers are evolved for dynamic game environments where limited inter-agent communication is used to drive spatial coordination of opponent teams. Given the complexity of the predator task, cooperative team behaviours are investigated. Initial candidates are generated using off-line learning procedures operating on minimal neural controllers with the aim of maximising opponent performance. These example controllers are then adapted using on-line (i.e. during play) learning techniques to yield opponents that provide games of high interest. The on-line learning methodology is evaluated using two dissimilar predator/prey games with a number of different computer player strategies. It exhibits generality across the two game test-beds and robustness to changes of player, initial opponent controller selected, and complexity of the game field. The interest metric is also evaluated by comparison with human judgement of game satisfaction in an experimental survey. A statistically significant number of players were asked to rank game experiences with a test-bed game using perceived interestingness and their ranking was compared with that of the proposed interest metric. The results show that the interest metric is consistent with human judgement of game satisfaction. Finally, the generality, limitations and potential of the proposed methodology and techniques are discussed, and other factors affecting the player’s satisfaction, such as the player’s own strategy, are briefly considered. Future directions building on the work described herein are presented and discussed.

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http://hdl.handle.net/1842/879

en

University of Edinburgh. College of Science and Engineering. School of Informatics.

AI

computer game

AI in Computer Games: Generating Interesting Interactive Opponents by the use of Evolutionary Computation

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy