Modeling opponent actions for table-tennis playing robot

Zhikun Wang, Abdeslam Boularias, Katharina Mülling, Jan Peters

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Opponent modeling is a critical mechanism in repeated games. It allows a player to adapt its strategy in order to better respond to the presumed preferences of its opponents. We introduce a modeling technique that adaptively balances safety and exploitability. The opponent's strategy is modeled with a set of possible strategies that contains the actual one with high probability. The algorithm is safe as the expected payoff is above the minimax payoff with high probability, and can exploit the opponent's preferences when sufficient observations are obtained. We apply the algorithm to a robot table-tennis setting where the robot player learns to prepare to return a served ball. By modeling the human players, the robot chooses a forehand, backhand or middle preparation pose before they serve. The learned strategies can exploit the opponent's preferences, leading to a higher rate of successful returns.

Original languageEnglish (US)
Title of host publicationAAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference
Pages1828-1829
Number of pages2
StatePublished - Nov 2 2011
Externally publishedYes
Event25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11 - San Francisco, CA, United States
Duration: Aug 7 2011Aug 11 2011

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Other

Other25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11
CountryUnited States
CitySan Francisco, CA
Period8/7/118/11/11

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    Wang, Z., Boularias, A., Mülling, K., & Peters, J. (2011). Modeling opponent actions for table-tennis playing robot. In AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference (pp. 1828-1829). (Proceedings of the National Conference on Artificial Intelligence; Vol. 2).