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Player Modeling, Search Algorithms and Strategies in Multi-player Games

  • Ulf Lorenz
  • Tobias Tscheuschner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4250)

Abstract

For a long period of time, two person zero-sum games have been in the focus of researchers of various communities. The efforts were mainly driven by the fascination of special competitions such as Deep Blue vs. Kasparov, and of the beauty of parlor games such as Checkers, Backgammon, Othello, and Go.

Multi-player games, however, have been investigated considerably less, and although literature of game theory fills books about equilibrium strategies in such games, practical experiences are rare. Recently, Korf, Sturtevant and a few others started highly interesting research activities. We focused on investigating a four-person chess variant, in order to understand the peculiarities of multi-player games without chance components. In this contribution, we present player models and search algorithms that we tested in the four-player chess world. As a result, we may state that the more successful player models can benefit from more efficient algorithms and speed, because searching more deeply leads to better results. Moreover, we present a meta-strategy, which beats a paranoid α-β player, the best known player in multi-player games.

Keywords

Game Tree Player Type Search Depth Strong Player Chess 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

  • Ulf Lorenz
    • 1
  • Tobias Tscheuschner
    • 1
  1. 1.Department of Computer SciencePaderbornGermany

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