Playing the SOS Game Using Feasible Greedy Strategy

Authors

DOI:

https://doi.org/10.21512/commit.v14i1.6167

Keywords:

SOS Game, Feasible Greedy Agent, Greedy Strategy, Game Tree

Abstract

The research aims to make an intelligent agent that can compete against the human player. In this research, the feasible greedy strategy is proposed to make an intelligent agent by checking all possible solutions in the limited tree levels to find effective movement. Several matches are conducted to evaluate the performance of the feasible greedy agent. The board size for the evaluation consists of 33, 44, 55, 66, 77, and 88 squares. From the result, the feasible greedy agent never loses against the random agent and the pure greedy agent. In 3 3 squares match, the agent can compensate against the human player, so the game always ends with a draw. In 44, 55, 66, 77, and 88 squares matches, the feasible greedy agent slightly outplays the human player.

Dimensions

Plum Analytics

Author Biography

Abas Setiawan, Universitas Dian Nuswantoro

Department of Informatics Engineering, Faculty of Computer Science

References

T. S. Ferguson, Game theory. Los Angeles: Mathematics Department, UCLA, 2014.

R. J. Gould, Mathematics in games, sports, and gambling: The games people play. Boca Raton: CRC Press, 2015.

E. K. Donkoh, R. Davis, E. D. Owusu-Ansah, E. A. Antwi, and M. Mensah, “Application of combinatorial techniques to the Ghanaian boardgame Zaminamina draft,” European Journal of Pure and Applied Mathematics, vol. 12, no. 1, pp. 159–175, 2019.

D. Keiser and B. McGee, Patterns - Literature, arts, and science. Prufrock Press Inc., 2008.

B. E. Aydin, H. Hagedooren, M. M. Rutten, J. Delsman, G. H. Oude Essink, N. van de Giesen, and E. Abraham, “A greedy algorithm for optimal sensor placement to estimate salinity in polder networks,” Water, vol. 11, no. 5, pp. 1–17, 2019.

Z. Dong, N. Liu, and R. Rojas-Cessa, “Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers,” Journal of Cloud Computing, vol. 4, no. 1, pp. 1–14, 2015.

C. Yang, T. Zheng, Z. Zhao, X. He, X. Zhang, X. Xiao, and J. Wang, “A greedy algorithm for detecting mutually exclusive patterns in cancer mutation data,” in International Work-Conference on Bioinformatics and Biomedical Engineering. Granada, Spain: Springer, May 8–10, 2019, pp. 154–165.

K. Lichy, M. Mazur, J. Stolarek, and P. Lipi´nski, “The use of heuristic algorithms: A case study of a card game,” Journal of Applied Computer Science, vol. 26, no. 2, pp. 107–116, 2018.

I. Toma, C. E. Alexandru, M. Dascalu, P. Dessus, and S. Trausan Matu, “Semantic Boggle: A game for vocabulary acquisition,” in European Conference on Technology Enhanced Learning. Tallinn, Estonia: Springer, Sept. 12–15, 2017, pp. 606–609.

E. Butler, E. Torlak, and Z. Popovi´c, “Synthesizing interpretable strategies for solving puzzle games,” in Proceedings of the 12th International Conference on the Foundations of Digital Games, Hyannis, MA, USA, Aug. 14–17, 2017, pp. 1–10.

R. Garg and D. P. Nayak, “Game of tic-tac-toe: Simulation using Min-Max algorithm,” International Journal of Advanced Research in Computer Science, vol. 8, no. 7, pp. 1074–1077, 2017.

P. Borovska and M. Lazarova, “Efficiency of parallel Minimax algorithm for game tree search,” in CompSysTech ’07: Proceedings of the 2007 international conference on Computer systems and technologies, University of Rousse, Bulgaria, June 14–15, 2007, pp. 1–6.

S. Jain and N. Khera, “An intelligent method forsolving tic-tac-toe problem,” in International Conference on Computing, Communication & Automation. Noida, India: IEEE, May 15–16, 2015, pp. 181–184.

S. Garg and D. Songara, “The winner decision model of tic tac toe game by using multi-tape turing machine,” in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Jaipur, India: IEEE, Sept. 21–24, 2016, pp. 573–579.

S. Garg, D. Songara, and S. Maheshwari, “The winning strategy of tic tac toe game model by using theoretical computer science,” in 2017 International Conference on Computer, Communications and Electronics (Comptelix). Jaipur, India: IEEE, July 1–2, 2017, pp. 89–95.

D. Draskovic, M. Brzakovic, and B. Nikolic, “A comparison of machine leaming methods using a two player board game,” in IEEE EUROCON 2019-18th International Conference on Smart Technologies. Novi Sad, Serbia: IEEE, July 1–4, 2019, pp. 1–5.

Downloads

Published

2020-05-31
Abstract 730  .
PDF downloaded 367  .