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  Balancing Safety and Exploitability in Opponent Modeling

Wang, Z., Boularias, A., Mülling, K., & Peters, J. (2011). Balancing Safety and Exploitability in Opponent Modeling. In Twenty-Fifth AAAI Conference on Artificial Intelligence (pp. 1515-1520). Menlo Park, CA, USA: AAAI Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BAD0-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-062E-5
Genre: Conference Paper

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 Creators:
Wang, Z1, Author              
Boularias, A1, Author              
Mülling, K1, Author              
Peters, J1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 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 his opponents. We introduce a new modeling technique that adaptively balances exploitability and risk reduction. An opponent’s strategy is modeled with a set of possible strategies that contain the actual strategy with a high probability. The algorithm is safe as the expected payoff is above the minimax payoff with a high probability, and can exploit the opponents’ preferences when sufficient observations have been obtained. We apply them to normal-form games and stochastic games with a finite number of stages. The performance of the proposed approach is first demonstrated on repeated rock-paper-scissors games. Subsequently, the approach is evaluated in a human-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.

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 Dates: 2011-08
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: BibTex Citekey: WangBMP2011
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Title: Twenty-Fifth AAAI Conference on Artificial Intelligence
Place of Event: San Francisco, CA, USA
Start-/End Date: 2011-08-07 - 2011-08-11

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Title: Twenty-Fifth AAAI Conference on Artificial Intelligence
Source Genre: Proceedings
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Publ. Info: Menlo Park, CA, USA : AAAI Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1515 - 1520 Identifier: ISBN: 978-1-577-35507-6