Effective and Diverse Adaptive Game AI

Adaptive techniques tend to converge to a single optimum. For adaptive game AI, such convergence is often undesirable, as repetitive game AI is considered to be uninteresting for players. In this paper, we propose a method for automatically learning diverse but effective macros that can be used as c...

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Bibliographic Details
Published inIEEE transactions on computational intelligence and AI in games. Vol. 1; no. 1; pp. 16 - 27
Main Authors Szita, I., Ponsen, M., Spronck, P.
Format Journal Article
LanguageEnglish
Published IEEE 01.03.2009
Subjects
Adaptive control
Adaptive control systems
Artificial intelligence
Computational intelligence
Computational modeling
Computer simulation
Convergence
cross-entropy method (CEM)
dynamic scripting
game
Games
Humans
Learning
Optimization
Optimization methods
Programmable control
reinforcement learning
Scripts
Testing
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Summary:Adaptive techniques tend to converge to a single optimum. For adaptive game AI, such convergence is often undesirable, as repetitive game AI is considered to be uninteresting for players. In this paper, we propose a method for automatically learning diverse but effective macros that can be used as components of adaptive game AI scripts. Macros are learned by a cross-entropy method (CEM). This is a selection-based optimization method that, in our experiments, maximizes an interestingness measure. We demonstrate the approach in a computer role-playing game (CRPG) simulation with two duelling wizards, one of which is controlled by an adaptive game AI technique called ldquodynamic scripting.rdquo Our results show that the macros that we learned manage to increase both adaptivity and diversity of the scripts generated by dynamic scripting, while retaining playing strength.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1943-068X
1943-0698
DOI:10.1109/TCIAIG.2009.2018706