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Workshop on Computer Games

International Workshop on General Intelligence in Game-Playing Agents

CGW 2015, GIGA 2015: Computer Games pp 3–15Cite as

Challenges and Progress on Using Large Lossy Endgame Databases in Chinese Checkers

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 614))

Abstract

A common evaluation function for playing Chinese Checkers with two or more players has been the single-agent distance across the board. This is an abstraction of a perfect heuristic, because it ignores the interactions between the players in the game. Previous work has studied these heuristics for smaller versions of the game, including 6-piece data for a board with 49 locations and 81 locations which have 13.98 million and 324.5 million combinations respectively. The single-agent solution to the full game of Chinese Checkers has 81 locations and 10 pieces per player. This results in 1.88 trillion possible positions and is stored using 500 GB of disk space. In this paper we report results from a preliminary study on how to best use the data to improve the play of a Chinese Checkers program.

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Notes

  1. 1.

    Due to symmetry either the start or the goal state can be used, although the choice influences whether subsets of data can be efficiently loaded.

  2. 2.

    In our original implementation the algorithms were indistinguishable. Improving the efficiency of our TD learning, by taking advantage of the binary features, significantly improved the performance of the TD player.

  3. 3.

    It can change by more than one because we may jump over an opponent’s piece, which is not accounted for in the single-agent data.

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Acknowledgments

This paper benefited from research by a summer student, Evan Boucher, who worked on the problem of determining the true distance of a state from the goal given the modulo distance.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Denver, Denver, CO, USA

    Nathan R. Sturtevant

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  1. Nathan R. Sturtevant
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Correspondence to Nathan R. Sturtevant .

Editor information

Editors and Affiliations

  1. Université Paris-Dauphine, Paris, France

    Tristan Cazenave

  2. Maastricht University, Maastricht, The Netherlands

    Mark H.M. Winands

  3. Universität Bremen, Bremen, Bremen, Germany

    Stefan Edelkamp

  4. Reykjavik University, Reykjavik, Iceland

    Stephan Schiffel

  5. The University of New South Wales, Sydney, New South Wales, Australia

    Michael Thielscher

  6. New York University, Brooklyn, New York, USA

    Julian Togelius

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Sturtevant, N.R. (2016). Challenges and Progress on Using Large Lossy Endgame Databases in Chinese Checkers. In: Cazenave, T., Winands, M., Edelkamp, S., Schiffel, S., Thielscher, M., Togelius, J. (eds) Computer Games. CGW GIGA 2015 2015. Communications in Computer and Information Science, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-39402-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-39402-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39401-5

  • Online ISBN: 978-3-319-39402-2

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