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|Title: ||AI in Computer Games: Generating Interesting Interactive Opponents by the use of Evolutionary Computation|
|Authors: ||Yannakakis, Georgios N.|
|Supervisor(s): ||Hallam, John|
|Issue Date: ||Dec-2005|
|Publisher: ||University of Edinburgh. College of Science and Engineering. School of Informatics.|
|Abstract: ||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
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.|
|Description: ||Institute of Perception, Action and Behaviour|
|Appears in Collections:||Informatics thesis and dissertation collection|
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