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Real-time imitation-based adaptation of gaming behaviour in modern computer games

Authors:

Steffen Priesterjahn,

Alexander Weimer,

Markus EberlingAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1431 - 1432

https://doi.org/10.1145/1389095.1389374

Published: 12 July 2008 Publication History

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Abstract

In the course of the recent complexification and sophistication of commercial computer games, the creation of competitive artificial players that are able to behave intelligently and successfully in the featured highly dynamic and complex virtual worlds has become a considerable challenge. This paper describes an evolutionary real-time adaptation approach to produce competitive artificial players in an action game. The proposed method is inspired by the idea of social learning or cultural evolution. Thus, the agents try to adapt to the level of their opponents by the exchange of information about advantageous behaviours within the population. In addition, the behaviour of the opponents and other players is recorded and used to create more sophisticated and human-like agents.

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S. Priesterjahn. Online Adaptation and Imitation in Modern Computer Games. PhD thesis, University of Paderborn, 2008.

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S. Priesterjahn and A. Weimer. An Evolutionary Online Adaptation Method for Modern Computer Games. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'07), 2007.

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Cited By

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Whiteson S(2012)Evolutionary Computation for Reinforcement LearningReinforcement Learning10.1007/978-3-642-27645-3_10(325-355)Online publication date: 2012
https://doi.org/10.1007/978-3-642-27645-3_10
Koppejan RWhiteson S(2011)Neuroevolutionary reinforcement learning for generalized control of simulated helicoptersEvolutionary Intelligence10.1007/s12065-011-0066-z4:4(219-241)Online publication date: 30-Oct-2011
https://doi.org/10.1007/s12065-011-0066-z
Cardamone LLoiacono DLanzi P(2010)Learning to Drive in the Open Racing Car Simulator Using Online NeuroevolutionIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2010.20521022:3(176-190)Online publication date: Sep-2010
https://doi.org/10.1109/TCIAIG.2010.2052102
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Index Terms

Real-time imitation-based adaptation of gaming behaviour in modern computer games
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents
      2. Multi-agent systems
  2. Machine learning
    1. Learning settings

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Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Whiteson S(2012)Evolutionary Computation for Reinforcement LearningReinforcement Learning10.1007/978-3-642-27645-3_10(325-355)Online publication date: 2012
https://doi.org/10.1007/978-3-642-27645-3_10
Koppejan RWhiteson S(2011)Neuroevolutionary reinforcement learning for generalized control of simulated helicoptersEvolutionary Intelligence10.1007/s12065-011-0066-z4:4(219-241)Online publication date: 30-Oct-2011
https://doi.org/10.1007/s12065-011-0066-z
Cardamone LLoiacono DLanzi P(2010)Learning to Drive in the Open Racing Car Simulator Using Online NeuroevolutionIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2010.20521022:3(176-190)Online publication date: Sep-2010
https://doi.org/10.1109/TCIAIG.2010.2052102
Galli LLoiacono DLanzi P(2009)Learning a context-aware weapon selection policy for unreal tournament IIIProceedings of the 5th international conference on Computational Intelligence and Games10.5555/1719293.1719346(310-316)Online publication date: 7-Sep-2009
https://dl.acm.org/doi/10.5555/1719293.1719346
Galli LLoiacono DLanzi P(2009)Learning a context-aware weapon selection policy for Unreal Tournament III2009 IEEE Symposium on Computational Intelligence and Games10.1109/CIG.2009.5286461(310-316)Online publication date: Sep-2009
https://doi.org/10.1109/CIG.2009.5286461

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AI Coach for Battle Royale Games

Artificial Intelligence Techniques on Real-time Strategy Games

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