Clash Royale Challenge: how to select training decks for win-rate prediction

• Autorzy: Janusz Andrzej , Grad Łukasz , Grzegorowski Marek

Identyfikatory

DOI

10.15439/2019F365

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

We summarize the sixth data mining competition organized at the Knowledge Pit platform in association with the Federated Conference on Computer Science and Information Systems series, titled Clash Royale Challenge: How to Select Training Decks for Win-rate Prediction. We outline the scope of this challenge and briefly present its results. We also discuss the problem of acquiring knowledge about new notions from video games through an active learning cycle. We explain how this task is related to the problem considered in the challenge and share results of experiments that we conducted to demonstrate usefulness of the active learning approach in practice.

Słowa kluczowe

EN

data mining contest; training subset selection; win-rates prediction; active learning; Clash Royale

PL

eksploracja danych; aktywne uczenie się; prawdopodobieństwo wygranej; Clash Royale; gra komputerowa

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 3--6
• Opis fizyczny: Bibliogr. 13 poz., rys., wz., wykr.

Twórcy

autor

Janusz Andrzej

• Institute of Informatics, University of Warsaw, Poland
• Esensei Sp. z o.o., Poland

autor

Grad Łukasz

• Institute of Informatics, University of Warsaw, Poland
• Esensei Sp. z o.o., Poland

autor

Grzegorowski Marek

• Institute of Informatics, University of Warsaw, Poland
• Esensei Sp. z o.o., Poland

Bibliografia

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• 3. B. Settles, Active Learning. Morgan & Claypool Publishers, 2012.
• 4. E. Lughofer, “Hybrid active learning for reducing the annotation effort of operators in classification systems,” Pattern Recogn., vol. 45, no. 2, pp. 884–896, Feb. 2012. [Online]. Available: http://dx.doi.org/10.1016/j.patcog.2011.08.009
• 5. W. Cai, Y. Zhang, Y. Zhang, S. Zhou, W. Wang, Z. Chen, and C. Ding, “Active learning for classification with maximum model change,” ACM Trans. Inf. Syst., vol. 36, no. 2, pp. 15:1–15:28, Aug. 2017. [Online]. Available: http://doi.acm.org/10.1145/3086820
• 6. H. T. Nguyen and A. Smeulders, “Active learning using pre-clustering,” in Proceedings of the Twenty-first International Conference on Machine Learning, ser. ICML ’04. New York, NY, USA: ACM, 2004, pp. 79–. [Online]. Available: http://doi.acm.org/10.1145/1015330.1015349
• 7. K. Konyushkova, R. Sznitman, and P. Fua, “Learning active learning from real and synthetic data,” CoRR, vol. abs/1703.03365, 2017. [Online]. Available: http://arxiv.org/abs/1703.03365
• 8. C. Zhang and K. Chaudhuri, “Active learning from weak and strong labelers,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, ser. NIPS’15. Cambridge, MA, USA: MIT Press, 2015, pp. 703–711. [Online]. Available: http://dl.acm.org/citation.cfm?id=2969239.2969318
• 9. A. Janusz, T. Tajmajer, M. Świechowski, Ł. Grad, J. Puczniewski, and D. Ślęzak, “Toward an intelligent HS deck advisor: Lessons learned from aaia’18 data mining competition,” in Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018, Poznań, Poland, September 9-12, 2018., M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., 2018, pp. 189–192. [Online]. Available: https://doi.org/10.15439/2018F386
• 10. A. Janusz, D. Ślęzak, S. Stawicki, and K. Stencel, “SENSEI: an intelligent advisory system for the esport community and casual players,” in 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018, Santiago, Chile, December 3-6, 2018. IEEE Computer Society, 2018, pp. 754–757. [Online]. Available: https://doi.org/10.1109/WI.2018.00010
• 11. A. J. Smola and B. Schölkopf, “A Tutorial on Support Vector Regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004.
• 12. D. Ruta, L. Cen, and Q. H. Vu, “Greedy incremental support vector regression,” in Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019, Leipzig, Germany, September 1-4, 2019., M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., 2019.
• 13. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. MIT Press, 2006.

Uwagi

1. This research was co-funded by Smart Growth Operational Programme 2014-2020, financed by European Regional Development Fund under GameINN project POIR.01.02.00-00-0184/17, operated by National Centre for Research and Development in Poland.

2. Track 1: Artificial Intelligence and Applications

3. Technical Session: 14th International Symposium Advances in Artificial Intelligence and Applications

4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).