[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2022.27.01.009

Analysis and simulator implementation of Mighty, an advanced imperfect information game

Lee, Jeongwon (Pebblous Inc.)
Kim, Kwihoon (Dept. of AI Convergence Education, Korea National University of Education)
Kim, Seung-Hyun (Dept. of Computer Education, Korea National University of Education)

Publication Information

Journal of the Korea Society of Computer and Information / v.27, no.1, 2022 , pp. 9-21 More about this Journal

Abstract

Mighty is an imperfect information game, similar to the internationally popular four-player card game Bridge, but more complex in terms of game rules and operation. An environment for exploring and analyzing the strategy of the Mighty Game is required, but compared to the development of many simulators for strategy analysis of other card games such as Bridge, there is no analysis tool for the Mighty Game. Even the definition and understanding of the Mighty game at the academic level is lacking. To solve these problems, this paper systematically defined the procedures and rules of the Mighty Game. And based on this definition, we implemented a simulator that can learn Mighty game and analyze various strategies. For the usability and accessibility of the service, the simulator was developed with JavaScript, and various analysis functions are provided in the web environment. Lastly, comparative analysis with other trick-taking games dealt with in the related research domain showed that the Mighty game has its value as an incomplete information game and that there are many characteristics that make it easy to apply AI-based learning methods.

Keywords

Mighty; Bridge; Game simulator; Trick-taking game; Imperfect information game;

Citations & Related Records

Reference

1	Denexa Games, "Mighty", https://www.denexa.com/blog/mighty
2	G. Qucheng, J. Yu, T. Yuandong, "Simple is better: Training an end-to-end contract bridge bidding agent without human knowledge", Real-world Sequential Decision Making workshop at ICML 2019, 2019.
3	H. Johannes, S. David, "Deep reinforcement learning from self-play in imperfect-information games," arXiv preprint arXiv:1603.01121, 2016.
4	S. Stefano, "Equilibrium Computation Techniques for Bridge," PhD Thesis. University of Illinois at Chicago, 2020.
5	Y. Chih-Kuan, H. Cheng-Yu, L. Hsuan-Tien, "Automatic bridge bidding using deep reinforcement learning," IEEE Transactions on Games, Vol. 10.4, pp. 365-377, 2018. https://doi.org/10.1109/tg.2018.2866036 DOI
6	F. Ian, "Search and Planning under Incomplete Information: a study using Bridge card play," Springer Science & Business Media, 2012. https://doi.org/10.1016/S0004-3702(97)00082-9 DOI
7	Deal 3.1, http://bridge.thomasoandrews.com/deal
8	Mighty Network 4.0, Celicath, https://tail41.snucse.org
9	Jack Jack Knave, https://jackjackknave.com/game/mighty
10	S. Nathan R, W. Adam M, "Feature construction for reinforcement learning in hearts," International Conference on Computers and Games. Springer, pp. 122-134, 2006. https://doi.org/10.1007/978-3-540-75538-8_11 DOI
11	W. Maxiem, "Learning to play the Game of Hearts using Reinforcement Learning and a Multi-Layer Perceptron," PhD Thesis. University of Groningen. 2017.
12	R. Douglas, S. Christopher, B. Michael, "Learning policies from human data for skat," IEEE Conference on Games (CoG). pp. 1-8, 2019. https://doi.org/10.1109/CIG.2019.8848125 DOI
13	A. Kai, C. Antoine, T. Julian, "Alphastar: An evolutionary computation perspective," Proceedings of the genetic and evolutionary computation conference companion, pp. 314-315, 2019. https://doi.org/10.1145/3319619.3321894 DOI
14	Z. Ruiyong, et al. "Design and Development of Bridge AI bid Program based on Double-hand Solver," IEEE Chinese Control And Decision Conference (CCDC), pp. 6281-6286, 2019. https://doi.org/10.1109/CCDC.2019.8833178 DOI
15	BridgeBuff, http://www.bridgebuff.com
16	Dealmaster Pro, http://dealmaster.com
17	Ted's Bridge World, https://tedmuller.us/Bridge/Simulations.htm
18	Peter Cheung's Bridge Website, http://crystalwebsite.tripod.com
19	MightyPlayer by WithCat, https://code.google.com/archive/p/mightyplayer
20	Y. Cho, J. Chulmin, M. Kim, J. Pannell and D. Roth, "Mighty-card game rules," https://www.pagat.com/picture/mighty.html
21	O. Tyler A. The Game of Whist. 2013.
22	S. David, et al. "Mastering the game of go without human knowledge," Nature, Vol. 550.7676, pp. 354-359, 2017. https://doi.org/10.15368/theses.2018.47 DOI
23	W. Daniel, et al. "Integrating monte carlo tree search with knowledge-based methods to create engaging play in a commercial mobile game," Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2013. https://dl.acm.org/doi/10.5555/3014712.3014728 DOI