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Advice is Useful for Game AI: Experiments with Alpha-Beta Search Players in Shogi

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Advances in Computer Games (ACG 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12516))

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Abstract

In this paper, we present methods to strengthen a game AI using advice from other game AIs during game play. People can improve their strength with advice, such as finding better moves or avoiding mistakes. Therefore, to improve the performance of game AIs, we focused on advice. The issues to be considered are the definition of advice and mechanism with advice for move selection. In this paper, we propose that “advice” are moves selected by an adviser and propose a mechanism that makes a player search again when the player’s move is different from advice. We performed tournaments among the proposed systems and other methods against a single engine to compare the strength in shogi. We showed the effectiveness of the proposed method from the experimental results and demonstrated that game AIs can improve their strength with advice. In addition, we found that the advice from a weaker game AI is still useful for game AI.

This work was supported by JSPS Grant-in-Aid for Young Scientists (A) No. 17K12807.

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Notes

  1. 1.

    https://github.com/HiraokaTakuya/apery. Accessed May 2019.

  2. 2.

    https://github.com/yaneurao/YaneuraOu. Accessed May 2019.

  3. 3.

    https://github.com/gikou-official/Gikou. Accessed May 2019.

  4. 4.

    https://github.com/saihyou/nozomi. Accessed May 2019.

  5. 5.

    https://github.com/ynasu87/nnue. Accessed May 2019.

  6. 6.

    http://gps.tanaka.ecc.u-tokyo.ac.jp/gpsshogi/index.php?GPSFish. Accessed May 2019.

References

  1. Althöfer, I.: Decision support systems with multiple choice structure. In: Numbers, Information and Complexity, pp. 525–540. Springer, Heidelberg (2000)

    Google Scholar 

  2. Althöfer, I., Snatzke, R.G.: Playing games with multiple choice systems. In: International Conference on Computers and Games, pp. 142–153. Springer, Heidelberg (2002)

    Google Scholar 

  3. Hassabis, D.: Artificial intelligence: chess match of the century. Nature 544(7651), 413 (2017)

    Article  Google Scholar 

  4. Hoki, K., Kaneko, T.: Large-scale optimization for evaluation functions with minimax search. J. Artif. Intell. Res. 49(1), 527–568 (2014)

    Article  MathSciNet  Google Scholar 

  5. Iida, H., Sakuta, M., Rollason, J.: Computer shogi. Artif. Intell. 134(1–2), 121–144 (2002)

    Article  Google Scholar 

  6. Kasparov, G.: The chess master and the computer. New York Rev. Books 57(2), 16–19 (2010)

    Google Scholar 

  7. Marcolino, L.S., Jiang, A.X., Tambe, M.: Multi-agent team formation: diversity beats strength? In: IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, 3–9 August 2013, pp. 279–285 (2013)

    Google Scholar 

  8. Obata, T., Sugiyama, T., Hoki, K., Ito, T.: Consultation algorithm for computer shogi: Move decisions by majority. In: International Conference on Computers and Games, pp. 156–165. Springer, Heidelberg (2011)

    Google Scholar 

  9. Schaeffer, J.: The games computers (and people) play. Adv. Comput. 50, 189–266 (2000)

    Article  Google Scholar 

  10. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., Hassabis, D.: A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science 362(6419), 1140–1144 (2018)

    Article  MathSciNet  Google Scholar 

  11. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., Hassabis, D.: Mastering the game of go without human knowledge. Nature 550, 354–359 (2017)

    Article  Google Scholar 

  12. Sugiyama, T., Obata, T., Hoki, K., Ito, T.: Optimistic selection rule better than majority voting system. In: van den Herik, H.J., Iida, H., Plaat, A. (eds.) Computers and Games, Lecture Notes in Computer Science, vol. 6515, pp. 166–175. Springer, Heidelberg (2011)

    Google Scholar 

  13. Takeuchi, S.: Weighted majority voting with a heterogeneous system in the game of shogi. In: The 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI2018), pp. 122–125 (2018)

    Google Scholar 

  14. Takizawa, T.: Computer shogi 2012 through 2014. In: Game Programming Workshop 2014, vol. 2014, pp. 1–8 (2014)

    Google Scholar 

  15. Taylor, M.E., Carboni, N., Fachantidis, A., Vlahavas, I., Torrey, L.: Reinforcement learning agents providing advice in complex video games. Connect. Sci. 26(1), 45–63 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Kochi University of Technology, Kami, Japan

    Shogo Takeuchi

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  1. Shogo Takeuchi
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Correspondence to Shogo Takeuchi .

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Editors and Affiliations

  1. Université Paris-Dauphine, Paris, France

    Tristan Cazenave

  2. Leiden University, The Hague, The Netherlands

    Jaap van den Herik

  3. The University of New South Wales, Sydney, NSW, Australia

    Abdallah Saffidine

  4. National Chiao-Tung University, Hsin-Chu, Taiwan

    I-Chen Wu

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Takeuchi, S. (2020). Advice is Useful for Game AI: Experiments with Alpha-Beta Search Players in Shogi. In: Cazenave, T., van den Herik, J., Saffidine, A., Wu, IC. (eds) Advances in Computer Games. ACG 2019. Lecture Notes in Computer Science(), vol 12516. Springer, Cham. https://doi.org/10.1007/978-3-030-65883-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-65883-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65882-3

  • Online ISBN: 978-3-030-65883-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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