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A Monte Carlo Algorithm for Time-Constrained General Game Playing

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14195))

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Abstract

General Game Playing (GGP) is a challenging domain for AI agents, as it requires them to play diverse games without prior knowledge. In this paper, we develop a strategy to improve move suggestions in time-constrained GGP settings. This strategy consists of a hybrid version of UCT that combines Sequential Halving and , favoring information acquisition in the root node, rather than overspend time on the most rewarding actions. Empirical evaluation using a GGP competition scheme from the Ludii framework shows that our strategy improves the average payoff over the entire competition set of games. Moreover, our agent makes better use of extended time budgets, when available.

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Notes

  1. 1.

    https://github.com/schererl/FinalYearProject.

  2. 2.

    https://github.com/Ludeme/LudiiAICompetition.

  3. 3.

    In contrast with exploiting policies, that allocate most resources to the most promising choice, non-exploiting policies allocate resources uniformly among choices, iteratively discarding the poorly-performing ones.

References

  1. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2), 235–256 (2002)

    Article  MATH  Google Scholar 

  2. Brice, W.C.: A history of board-games other than chess. by h. j. r. murray. oxford: Clarendon press, 1952. pp. viii 287, 86 text figs. 42s. J. Hellenic Stud. 74, 219–219 (1954). https://doi.org/10.2307/627627

  3. Bubeck, S., Munos, R., Stoltz, G.: Pure exploration in finitely-armed and continuous-armed bandits. Theoret. Comput. Sci. 412(19), 1832–1852 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  4. Cazenave, T.: Sequential halving applied to trees. IEEE Trans. Comput. Intell. AI Games 7(1), 102–105 (2014)

    Article  Google Scholar 

  5. Genesereth, M., Love, N., Pell, B.: General game playing: overview of the AAAI competition. AI Mag. 26(2), 62–62 (2005)

    Google Scholar 

  6. Karnin, Z., Koren, T., Somekh, O.: Almost optimal exploration in multi-armed bandits. In: International Conference on Machine Learning, pp. 1238–1246. PMLR (2013)

    Google Scholar 

  7. Kocsis, L., Szepesvári, C., Willemson, J.: Improved monte-carlo search. Univ. Tartu, Estonia, Tech. Rep. 1, 1–22 (2006)

    Google Scholar 

  8. Parlett, D.: The Oxford history of board games. Oxford University Press, Oxford (1999)

    Google Scholar 

  9. Pepels, T.: Novel selection methods for monte-carlo tree search. Master’s thesis, Department of Knowledge Engineering, Maastricht University, Maastricht, The Netherlands (2014)

    Google Scholar 

  10. Pepels, T., Cazenave, T., Winands, M.H., Lanctot, M.: Minimizing simple and cumulative regret in monte-carlo tree search. In: Workshop on Computer Games, pp. 1–15. Springer (2014)

    Google Scholar 

  11. Perez Liebana, D., Dieskau, J., Hunermund, M., Mostaghim, S., Lucas, S.: Open loop search for general video game playing. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 337–344 (2015)

    Google Scholar 

  12. Piette, É., Soemers, D.J.N.J., Stephenson, M., Sironi, C.F., Winands, M.H.M., Browne, C.: Ludii - the ludemic general game system. In: Giacomo, G.D., Catala, A., Dilkina, B., Milano, M., Barro, S., Bugarín, A., Lang, J. (eds.) Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020). vol. 325, pp. 411–418. IOS Press (2020)

    Google Scholar 

  13. Świechowski, M., Godlewski, K., Sawicki, B., Mańdziuk, J.: Monte carlo tree search: A review of recent modifications and applications. Artificial Intelligence Review, pp. 1–66 (2022)

    Google Scholar 

  14. Świechowski, M., Park, H., Mańdziuk, J., Kim, K.J.: Recent advances in general game playing. Sci. World J. 2015 (2015)

    Google Scholar 

  15. Tolpin, D., Shimony, S.: Mcts based on simple regret. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 26.1, pp. 570–576 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil

    Victor Scherer Putrich & Felipe Meneguzzi

  2. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Anderson Rocha Tavares

  3. University of Aberdeen, Aberdeen, Scotland

    Felipe Meneguzzi

Authors
  1. Victor Scherer Putrich
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  2. Anderson Rocha Tavares
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  3. Felipe Meneguzzi
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Corresponding author

Correspondence to Victor Scherer Putrich .

Editor information

Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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Putrich, V.S., Tavares, A.R., Meneguzzi, F. (2023). A Monte Carlo Algorithm for Time-Constrained General Game Playing. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14195. Springer, Cham. https://doi.org/10.1007/978-3-031-45368-7_7

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  • DOI: https://doi.org/10.1007/978-3-031-45368-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45367-0

  • Online ISBN: 978-3-031-45368-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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