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Towards manifold learning for gamebot behavior modeling

Published: 15 June 2005 Publication History

Abstract

Traditionally Computer Game Agent behaviors are generated by top-down approaches like finite state machines or scripts. So far, however, this had only mediocre success in creating life-like impressions. The bottom-up approach of imitation learning for agents has become very popular in recent robotics research and, in earlier work, we already discussed how imitation learning may apply to the programming of life-like computer game characters. However, so far we ignored problems concerning high dimensional state spaces for the most part, although behavior execution and learning takes place in such spaces.In this paper, we investigate the usage of non-linear dimensionality reduction for gamedata. We therefore focus on the aspect of topological gameworld representations and their dimensionality reduced counterparts. Dimensionality reduction is achieved by learning manifolds using Locally Linear Embedding. A mapping between data and embedding space is realized by Radial Basis Function interpolators. Experiments focus on movement path calculation and comparison in 3D and 2D embedding space world representations. The results indicate certain problems inherent to this approach but nevertheless justify further investigations.

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Cited By

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  • (2013)Behavioral-based cheating detection in online first person shooters using machine learning techniques2013 IEEE Conference on Computational Inteligence in Games (CIG)10.1109/CIG.2013.6633617(1-8)Online publication date: Aug-2013
  • (2010)Game Bot Detection via Avatar Trajectory AnalysisIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2010.20725062:3(162-175)Online publication date: Sep-2010
  • (2008)Game bot identification based on manifold learningProceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games10.1145/1517494.1517498(21-26)Online publication date: 21-Oct-2008

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cover image ACM Other conferences
ACE '05: Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology
June 2005
511 pages
ISBN:1595931104
DOI:10.1145/1178477
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: 15 June 2005

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Cited By

View all
  • (2013)Behavioral-based cheating detection in online first person shooters using machine learning techniques2013 IEEE Conference on Computational Inteligence in Games (CIG)10.1109/CIG.2013.6633617(1-8)Online publication date: Aug-2013
  • (2010)Game Bot Detection via Avatar Trajectory AnalysisIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2010.20725062:3(162-175)Online publication date: Sep-2010
  • (2008)Game bot identification based on manifold learningProceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games10.1145/1517494.1517498(21-26)Online publication date: 21-Oct-2008

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