Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F573

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 121–125 (2017)

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Abstract. This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge Pit platform. We briefly describe the scope and background of this competition in the context of a more general project related to the development of an AI engine for video games, called Grail. We also discuss the outcomes of this challenge and demonstrate how predictive models for the assessment of player's winning chances can be utilized in a construction of an intelligent agent for playing Hearthstone. Finally, we show a few selected machine learning approaches for modeling state and action values in Hearthstone. We provide evaluation for a few promising solutions that may be used to create more advanced types of agents, especially in conjunction with Monte Carlo Tree Search algorithms.

References

1. D. Taralla, “Learning artificial intelligence in large-scale video games: A first case study with hearthstone: Heroes of warcraft,” Ph.D. dissertation, Université de Liege, Liege, Belgium, 2015.
2. P. García-Sánchez, A. Tonda, G. Squillero, A. Mora, and J. J. Merelo, “Evolutionary deckbuilding in hearthstone,” in Computational Intelligence and Games (CIG), 2016 IEEE Conference on. IEEE, 2016, pp. 1–8.
3. D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al., “Mastering the game of go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.
4. A. Janusz, D. Ślęzak, S. Stawicki, and M. Rosiak, “Knowledge Pit - a data challenge platform,” in Proceedings of the 24th International Workshop on Concurrency, Specification and Programming, Rzeszow, Poland, September 28-30, 2015., 2015, pp. 191–195. [Online]. Available: http://ceur-ws.org/Vol-1492/Paper_18.pdf
5. S. Kaufman, S. Rosset, C. Perlich, and O. Stitelman, “Leakage in data mining: Formulation, detection, and avoidance,” TKDD, vol. 6, no. 4, p. 15, 2012.
6. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, ser. Springer Series in Statistics. New York, NY, USA: Springer New York Inc., 2001.
7. M. S. Szczuka and D. Ślęzak, “Feedforward neural networks for compound signals,” Theor. Comput. Sci., vol. 412, no. 42, pp. 5960– 5973, 2011.
8. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 25th International Conference on Neural Information Processing Systems, ser. NIPS’12. USA: Curran Associates Inc., 2012, pp. 1097–1105. [Online]. Available: http://dl.acm.org/citation.cfm?id=2999134.2999257
9. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, ser. CVPR ’14. Washington, DC, USA: IEEE Computer Society, 2014, pp. 1725–1732. [Online]. Available: http://dx.doi.org/10.1109/CVPR.2014.223
10. T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’16. New York, NY, USA: ACM, 2016, pp. 785–794. [Online]. Available: http://doi.acm.org/10.1145/2939672.2939785
11. A. Janusz, “Combining multiple predictive models using genetic algorithms,” Intelligent Data Analysis, vol. 16, no. 5, pp. 763–776, 2012. [Online]. Available: http://dx.doi.org/10.3233/IDA-2012-0550
12. A. Grużdź, A. Ihnatowicz, and D. Ślęzak, “Interactive gene clustering—a case study of breast cancer microarray data,” Information Systems Frontiers, vol. 8, no. 1, pp. 21–27, 2006.
13. C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton, “A Survey of Monte Carlo Tree Search Methods,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 4, no. 1, pp. 1–43, 2012.
14. S. Gelly, Y. Wang, O. Teytaud, M. U. Patterns, and P. Tao, “Modification of UCT with Patterns in Monte-Carlo Go,” 2006.
15. X. Cai and D. C. Wunsch II, “Computer Go: A Grand Challenge to AI,” in Challenges for Computational Intelligence. Springer, 2007, pp. 443–465.
16. M. Świechowski, H. Park, J. Mańdziuk, and K.-J. Kim, “Recent Advances in General Game Playing,” The Scientific World Journal, vol. 2015, 2015.
17. D. Perez, S. Samothrakis, and S. Lucas, “Knowledge-Based Fast Evolutionary MCTS for General Video Game Playing,” in 2014 IEEE Conference on Computational Intelligence and Games. IEEE, 2014, pp. 1–8.
18. L. Kocsis and C. Szepesvári, “Bandit Based Monte-Carlo Planning,” in Proceedings of the 17th European conference on Machine Learning, ser. ECML’06. Berlin, Heidelberg: Springer-Verlag, 2006, pp. 282–293.
19. K. Walędzik and J. Mańdziuk, “An automatically generated evaluation function in general game playing,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 6, no. 3, pp. 258–270, Sept 2014.
20. G. M. J. Chaslot, M. H. Winands, H. J. V. D. HERIK, J. W. Uiterwijk, and B. Bouzy, “Progressive strategies for monte-carlo tree search,” New Mathematics and Natural Computation, vol. 4, no. 03, pp. 343–357, 2008.
21. M. Świechowski and J. Mańdziuk, “Self-Adaptation of Playing Strategies in General Game Playing,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 6, no. 4, pp. 367–381, Dec 2014.
22. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. [Online]. Available: http://dx.doi.org/10.1162/neco.1997.9.8.1735