Abstract
The game theory is widely acknowledged to benefit a lot from recent advances in deep learning, and intelligent competition strategies have been proposed for both complete information games and incomplete information games in recent years. In this paper, the four-players Chinese Mahjong game, which is a typical incomplete information game, is emphasized, a low-level semantic pseudo image generated based on game related prior knowledge and a novel deep residual network-based competition strategy are introduced to play the Chines Mahjong game. Technically, the deep learning within this new competition strategy is realized by a series of “GoBlock”, which is a new deep learning model structure introduced in this paper. Also, the “GoBlock” is further made up of several “Inception+” sub-structures, which is novel as well. Comprehensive experiments are conducted to reveal the superiority of this new competition strategy. A great number of the Chinese Mahjong game data have been collected from an online Chinese Mahjong company to construct the dataset in this study, and the newly proposed competition strategy has been compared with several shallow learning-based methods as well as deep learning-based methods. Both qualitative and quantitative analysis have been conducted based on outcomes obtained by all compared methods, and the superiority of the new competition strategy over others are suggested. Furthermore, an interesting competition among the new AI competition strategy and three real senior players are also introduced in this paper. The effectiveness and efficiency of the new competition strategy over real senior human players are also revealed by quantitative analysis based on four measures, from the statistical point of view. It is also necessary to point out that, this work is the first attempt to tackle the Mahjong game, which is a typical incomplete information game, from the deep learning perspective.
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Bahdanau D, Chorowski J, Serdyuk D, Brakel P, Bengio Y (2015) End-to-end attention-based large vocabulary speech recognition. https://doi.org/10.1109/icassp.2016.7472618
Bansal T, Pachocki J, Sidor S et al (2017) Emergent complexity via multi-agent competition. arXiv:1710.03748
Bowling M, Burch N, Johanson M, Tammelin O (2017) Heads-up limit hold’em poker is solved. Science 347(6218):145–149. https://doi.org/10.1145/3131284
Brown N, Sandholm T (2017) Reduced space and faster convergence in imperfect-information games via pruning. In: International conference on machine learning, pp 596–604. http://proceedings.mlr.press/v70/brown17a.html
Brown N, Sandholm T (2017) Safe and nested subgame solving for imperfect-information games. In: Advances in neural information processing systems, pp 689–699. arXiv:1705.02955
Chen Y, Li J, Xiao H, Jin X, Yan S, Feng J (2017) Dual path networks. arXiv:1707.01629
Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H et al (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. Computer Science, https://doi.org/10.3115/v1/d14-1179
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1007/BF00994018
Drachen A, Yancey M, Maguire J, Chu D, Wang IY, Mahlmann T, Klabajan D (2014) Skill-based differences in spatio-temporal team behaviour in defence of the ancients 2 (dota 2). In: Games media entertainment (GEM), 2014 IEEE. IEEE, pp 1–8. https://doi.org/10.1109/gem.2014.7048109
Figurnov M, Collins MD, Zhu Y, Zhang L, Huang J, Vetrov DP, Salakhutdinov R (2017) Spatially adaptive computation time for residual networks. In: CVPR, vol 2, p 7. https://doi.org/10.1109/cvpr.2017.194
Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with lstm. Neural Comput 12(10):2451–2471. https://doi.org/10.1049/cp:19991218
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition, pp 770–778, https://doi.org/10.1109/cvpr.2016.90
He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision. Springer, Cham, pp 630–645. https://doi.org/10.1007/978-3-319-46493-0_38
Heinrich J, Silver D (2016) Deep reinforcement learning from self-play in imperfect-information games. arXiv:1603.01121
Helpman E (1987) Imperfect competition and international trade: evidence from fourteen industrial countries. J Jpn Int Econ 1(1):62–81. https://doi.org/10.1016/0889-1583(87)90027-X
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks, vol 1. CVPR, p 3. https://doi.org/10.1109/cvpr.2017.243
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167
Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision. Springer, Cham, pp 694–711. https://doi.org/10.1007/978-3-319-46475-6_43
Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv:1312.6114
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980
Kingma DP, Dhariwal P (2018) Glow: generative flow with invertible 1x1 convolutions. arXiv:1807.03039
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: International conference on neural information processing systems, vol 60. Curran Associates Inc, pp 1097–1105. https://doi.org/10.1145/3065386
Mason L, Baxter J, Bartlett PL, Frean MR (2000) Boosting algorithms as gradient descent. In: Advances in neural information processing systems, pp 512–518. https://dblp.org/rec/conf/nips/MasonBBF99
Mizukami N, Tsuruoka Y (2015) Building a computer Mahjong player based on Monte Carlo simulation and opponent models. In: IEEE conference on computational intelligence and games. IEEE, pp 275–283. https://doi.org/10.1109/cig.2015.7317929
Moračík M, Schmid M, Burch N, Lisý V., Morrill D, Bard N et al (2017) Deepstack: expert-level artificial intelligence in heads-up no-limit poker. Science 356(6337):508. https://doi.org/10.1126/science.aam6960
Nash J (1951) Non-cooperative games. Ann Math 54(2):286–295. https://doi.org/10.1515/9781400884087-009
Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484–489. https://doi.org/10.1038/nature16961
Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A et al (2017) Mastering the game of go without human knowledge. Nature 550 (7676):354–359. https://doi.org/10.1038/nature24270
Silver D, Hubert T, Schrittwieser J, Antonoglou I, Lai M, Guez A (2017) Mastering chess and shogi by self-play with a general reinforcement learning algorithm, Lillicrap, T, arXiv:1712.01815
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9. https://doi.org/10.1109/cvpr.2015.7298594
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826. https://doi.org/10.1109/cvpr.2016.308
Vinyals O, Ewalds T, Bartunov S, Georgiev P, Vezhnevets AS, Yeo M, Quan J (2017) Starcraft ii: a new challenge for reinforcement learning. arXiv:1708.04782
Von Neumann J (1959) On the theory of games of strategy. Contributions to the Theory of Games 4:13–42. https://doi.org/10.1515/9781400882168-003
Wang C, Yang H, Bartz C, Meinel C (2016) Image captioning with deep bidirectional lstms, https://doi.org/10.1145/2964284.2964299
Wang C (2017) RRA: recurrent residual attention for sequence learning. arXiv:1709.03714
Wang C, Yang H, Meinel C (2018) Image captioning with deep bidirectional lstms and multi-task learning. ACM Trans Multimed Comput Commun Appl 14(2s):1–20. https://doi.org/10.1145/3115432
Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Klingner J (2016) Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv:1609.08144
Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 5987–5995. https://doi.org/10.1109/cvpr.2017.634
Zagoruyko S, Komodakis N (2016) Wide residual networks. arXiv:1605.07146, https://doi.org/10.5244/c.30.87
Zambaldi V, Raposo D, Santoro A, Bapst V, Li Y, Babuschkin I, Shanahan M (2018) Relational deep reinforcement learning. arXiv:1806.01830
Acknowledgements
The authors would like to acknowledge the grant 61862043 approved by National Natural Science Foundation of China, key grants 20181ACB20006 and 20171ACB21017 as well as grant 20161BAB212047 approved by Natural Science Foundation of Jiangxi Province for supporting this study.
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Wang, M., Yan, T., Luo, M. et al. A novel deep residual network-based incomplete information competition strategy for four-players Mahjong games. Multimed Tools Appl 78, 23443–23467 (2019). https://doi.org/10.1007/s11042-019-7682-5
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DOI: https://doi.org/10.1007/s11042-019-7682-5