Application of machine learning to help AI to play Hearthstone

• Autorzy: Patekha Evgeny

Identyfikatory

DOI

10.15439/2017F566

• Konferencja: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems
• Języki publikacji: EN

Abstrakty

EN

This paper presents a solution, which was developed as a part of the competition AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone. The goal of the competition was to predict the probability of AI player win in different intra-game states of Hearthstone game (online computer game with cards). This solution got the third place at the final leaderboard. The paper describes models and local validation approach, which was very useful for models development without overfitting.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2017
• Tom: Vol. 13
• Strony: 45--48
• Opis fizyczny: Bibliogr. 13 poz., tab.

Twórcy

autor

Patekha Evgeny

Bibliografia

• 1. Hearthstone: Heroes of Warcraft. [Online]. Available: http://eu.battle.net/hearthstone/en/
• 2. Blizzard Entertainment. [Online]. Available: http://www.blizzard.com
• 3. Hearthstone wiki. [Online]. Available: http://hearthstone.gamepedia.com
• 4. International Symposium Advances in Artificial Intelligence and Applications. [Online]. Available: https://fedcsis.org/2017/aaia
• 5. AAIA'17 Data Mining Challenge. [Online]. Available: https://knowledgepit.fedcsis.org/contest/view.php?id=120
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• 9. hearthstonejson.com. [Online]. Available: https://api.hearthstonejson.com/v1/18792/enUS/cards.json
• 10. Jerome H. Friedman, “Greedy function approximation: A gradient boosting machine.” Ann. Statist. 29 (2001), no. 5, 1189--1232. http://dx.doi.org/10.1214/aos/1013203451 [Online].Available: https://statweb.stanford.edu/~jhf/ftp/trebst.pdf
• 11. Tianqi Chen, Carlos Guestrin, “XGBoost: A Scalable Tree Boosting System” KDD ’16, August 13-17, 2016, San Francisco, CA, USA http://dx.doi.org/10.1145/2939672.2939785 [Online]. Available: https://arxiv.org/abs/1603.02754
• 12. N.Srivastava, G.E.Hinton, A.Krizhevsky, I.Sutskever, R.Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting” Journal of Machine Learning Research 15 (1). 1929-1958 [Online]. Available: http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
• 13. xgboost library. [Online]. Available: https://github.com/dmlc/xgboost