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Solving the Double Dummy Bridge Problem with Shallow Autoencoders

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11304))

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Abstract

This paper presents a new approach to solving the Double Dummy Bridge Problem (DDBP). The DDBP is a hard classification task utilized by bridge playing programs which rely on Monte Carlo simulations. The proposed method employs shallow autoencoders (AEs) during an unsupervised pretraining phase and Multilayer Perceptron networks (MLPs) with three hidden layers, built on top of these trained AEs, in the final fine-tuning training. The results are compared with our previous study in which MLPs with similar architectures, but with no use of AEs and pretraining, were employed to solve this task. Several conclusions concerning efficient weight topologies and fine-tuning schemes of the proposed model, as well as interesting weight patterns discovered in the trained networks are presented and explained.

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References

  1. DDBP Github repository. https://github.com/holgus103/DDBP/

  2. Amit, A., Markovitch, S.: Learning to bid in bridge. Mach. Learn. 63(3), 287–327 (2006)

    Article  Google Scholar 

  3. Beling, P.: Partition search revisited. IEEE Trans. Comput. Intell. AI Games 9(1), 76–87 (2017)

    Article  Google Scholar 

  4. David, O.E., Netanyahu, N.S., Wolf, L.: DeepChess: end-to-end deep neural network for automatic learning in chess. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9887, pp. 88–96. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44781-0_11

    Chapter  Google Scholar 

  5. Dharmalingam, M., Amalraj, R.: Articifial neural network architecture for solving the double dummy bridge problem in contract bridge. Int. J. Adv. Res. Comput. Commun. Eng. 2(12), 4683–4691 (2013)

    Google Scholar 

  6. Dharmalingam, M., Amalraj, R.: A solution to the double dummy contract bridge problem influenced by supervised learning module adapted by artificial neural network. ICTACT J. Soft Comput. 5, 836–843 (2014)

    Article  Google Scholar 

  7. Dharmalingam, M., Amalraj, R.: Supervised Elman neural network architecture for solving double dummy bridge problem in contract bridge. Int. J. Sci. Res. (IJSR) 3(6), 2745–2750 (2014)

    Google Scholar 

  8. Francis, H., Truscott, A., Francis, D. (eds.): The Official Encyclopedia of Bridge, 5th edn. American Contract Bridge League Inc., Memphis (1994)

    Google Scholar 

  9. Ginsberg, M.L.: http://www.gibware.com

  10. Ginsberg, M.L.: Library of double-dummy results. http://www.cirl.uoregon.edu/ginsberg/gibresearch.html

  11. Ho, C.Y., Lin, H.T.: Contract bridge bidding by learning. In: AAAI Workshop: Computer Poker and Imperfect Information (2015)

    Google Scholar 

  12. Mańdziuk, J., Mossakowski, K.: Example-based estimation of hand’s strength in the game of bridge with or without using explicit human knowledge. In: IEEE Symposium on Computational Intelligence in Data Mining, Honolulu, Hawaii, USA, pp. 413–420 (2007)

    Google Scholar 

  13. Mańdziuk, J., Mossakowski, K.: Neural networks compete with expert human players in solving the double dummy bridge problem. In: 2009 IEEE Symposium on Computational Intelligence and Games, pp. 117–124, September 2009

    Google Scholar 

  14. Mossakowski, K., Mańdziuk, J.: Artificial neural networks for solving double dummy bridge problems. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 915–921. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24844-6_142

    Chapter  MATH  Google Scholar 

  15. Mossakowski, K., Mańdziuk, J.: Neural networks and the estimation of hands’ strength in contract bridge. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 1189–1198. Springer, Heidelberg (2006). https://doi.org/10.1007/11785231_124

    Chapter  Google Scholar 

  16. Mossakowski, K., Mańdziuk, J.: Learning without human expertise: a case study of the double dummy bridge problem. IEEE Trans. Neural Netw. 20(2), 278–299 (2009)

    Article  Google Scholar 

  17. Muthusamy, D.: Double dummy bridge problem in contract bridge: an overview. Artif. Intell. Syst. Mach. Learn. 10(1), 1–7 (2018)

    Google Scholar 

  18. Ng, A., Ngiam, J., Foo, C.Y., Mai, Y., Suen, C.: UFLDL tutorial. http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial

  19. Yegnanarayana, B., Khemani, D., Sarkar, M.: Neural networks for contract bridge bidding. Sadhana 21(3), 395–413 (1996)

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Polish National Science Centre grant 2017/25/B/ST6/02061.

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Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland

    Jacek Mańdziuk & Jakub Suchan

Authors
  1. Jacek Mańdziuk
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  2. Jakub Suchan
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Corresponding author

Correspondence to Jacek Mańdziuk .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Mańdziuk, J., Suchan, J. (2018). Solving the Double Dummy Bridge Problem with Shallow Autoencoders. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_23

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  • DOI: https://doi.org/10.1007/978-3-030-04212-7_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04211-0

  • Online ISBN: 978-3-030-04212-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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