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Workshop on Computer Games

CGW 2018: Computer Games pp 98–115Cite as

Iterative Tree Search in General Game Playing with Incomplete Information

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1017))

Abstract

General Game Playing (GGP) is concerned with the development of programs capable of effectively playing a game by just receiving its rules and without human intervention. The standard game representation language GDL has recently been extended so as to include games with incomplete information. The so-called Lifted HyperPlay technique, which is based on model sampling, provides a state-of-the-art solution to general game playing with incomplete information. However, this method is known not to model opponents properly, with the effect that it generates only pure strategies and is short-sighted when valuing information. In this paper, we overcome these limitations by adapting the classic idea of fictitious play to introduce an Iterative Tree Search algorithm for incomplete-information GGP. We demonstrate both theoretically and experimentally that our algorithm provides an improvement over existing solutions on several classes of games that have been discussed in the literature.

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Notes

  1. 1.

    In game theory the term imperfect information is used to refer to the class of games in which players lack full information about the state of the game. On the other hand, in AI it is more common to use the expression incomplete information for problems in which agents lack full information. It has become customary in GGP to follow the standard AI terminology.

  2. 2.

    Each move is unique. Having similar names for moves at different states does not mean the moves are the same.

  3. 3.

    The replay function replays from the initial state to the given state. In the sequential ECW game, where the only information set belongs to the secondPlayer, the replay function will be reduced to a simple information set function.

  4. 4.

    Frank and Basin [7] have introduced the “Vector MiniMax” technique, which also just lessens, rather than completely avoids, the impact of non-locality.

  5. 5.

    This game can, of course, be straightforwardly axiomatized in GDL-II as a GGP-II game [22].

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Authors and Affiliations

  1. UNSW Sydney, Kensington, NSW, 2052, Australia

    Armin Chitizadeh & Michael Thielscher

Authors
  1. Armin Chitizadeh
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  2. Michael Thielscher
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Correspondence to Armin Chitizadeh .

Editor information

Editors and Affiliations

  1. LAMSADE, Université Paris-Dauphine, Paris, France

    Tristan Cazenave

  2. University of New South Wales, Sydney, Australia

    Abdallah Saffidine

  3. University of Alberta, Edmonton, Canada

    Nathan Sturtevant

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Chitizadeh, A., Thielscher, M. (2019). Iterative Tree Search in General Game Playing with Incomplete Information. In: Cazenave, T., Saffidine, A., Sturtevant, N. (eds) Computer Games. CGW 2018. Communications in Computer and Information Science, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-24337-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-24337-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24336-4

  • Online ISBN: 978-3-030-24337-1

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