Learning in One-Shot Strategic Form Games

  • Alon Altman
  • Avivit Bercovici-Boden
  • Moshe Tennenholtz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)


We propose a machine learning approach to action prediction in one-shot games. In contrast to the huge literature on learning in games where an agent’s model is deduced from its previous actions in a multi-stage game, we propose the idea of inferring correlations between agents’ actions in different one-shot games in order to predict an agent’s action in a game which she did not play yet. We define the approach and show, using real data obtained in experiments with human subjects, the feasibility of this approach. Furthermore, we demonstrate that this method can be used to increase payoffs of an adequately informed agent. This is, to the best of our knowledge, the first proposed and tested approach for learning in one-shot games, which is the most basic form of multi-agent interaction.


Association Rule Multiagent System Price Auction Trust Game Strategic Form Game 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alon Altman
    • 1
  • Avivit Bercovici-Boden
    • 1
  • Moshe Tennenholtz
    • 1
  1. 1.Faculty of Industrial Engineering and ManagementTechnion — Israel Institute of TechnologyHaifaIsrael

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