Further Sources for you Agent:

Original SabberStone Git-Repository

Dataset of Replays: Collect-o-Bot

 

Related Work:

Dockhorn, A., & Mostaghim, S. (2019). Introducing the Hearthstone-AI Competition, 1–4. Retrieved from http://arxiv.org/abs/1906.04238

Hoover, A. K., Togelius, J., Lee, S., & de Mesentier Silva, F. (2019). The Many AI Challenges of Hearthstone. KI – Künstliche Intelligenz. https://doi.org/10.1007/s13218-019-00615-z

Mesentier Silva, F. de, Canaan, R., Lee, S., Fontaine, M. C., Togelius, J., & Hoover, A. K. (2019). Evolving the Hearthstone Meta, 1–8. https://doi.org/10.1109/cig.2019.8847966

Choe, J. S. B., & Kim, J. (2019). Enhancing Monte Carlo Tree Search for Playing Hearthstone. In 2019 IEEE Conference on Games (CoG) (pp. 1–7). IEEE. https://doi.org/10.1109/CIG.2019.8848034

Dockhorn, A., Schwensfeier, T., & Kruse, R. (2019). Fuzzy Multiset Clustering for Metagame Analysis. In Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology. Available at: http://www.is.ovgu.de/is_media/Research/Publications/Eusflat_2019_Dockhorn-p-5164.pdf

García-Sánchez, P., Tonda, A., Fernández-Leiva, A. J., & Cotta, C. (2019). Optimizing Hearthstone agents using an evolutionary algorithm. Knowledge-Based Systems, 105032. https://doi.org/10.1016/j.knosys.2019.105032

Świechowski, M., Tajmajer, T. & Janusz, A. (2018). Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms. IEEE Conference on Computational Intelligence and Games, CIG 2018. https://arxiv.org/abs/1808.04794v1

García-Sánchez, P., Tonda, A., Mora, A. M., Squillero, G., & Merelo, J. J. (2018). Automated Playtesting in Collectible Card Games using Evolutionary Algorithms: a Case Study in HearthStone. Knowledge-Based Systemshttps://doi.org/10.1016/j.knosys.2018.04.030

Dockhorn, A., Frick, M., Akkaya, Ü., & Kruse, R. (2018). Predicting Opponent Moves for Improving Hearthstone AI. In 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018 (pp. 621–632). http://doi.org/10.1007/978-3-319-91476-3_51

Garcia-Sanchez, P., Tonda, A., Squillero, G., Mora, A., & Merelo, J. J. (2017). Evolutionary deckbuilding in hearthstone. IEEE Conference on Computatonal Intelligence and Games, CIG. http://doi.org/10.1109/CIG.2016.7860426

Santos, A., Santos, P. A., & Melo, F. S. (2017). Monte Carlo Tree Search Experiments in Hearthstone, (June).

Stiegler, A., Dahal, K., Maucher, J., & Livingstone, D. (2017). Symbolic Reasoning for Hearthstone. IEEE Transactions on Computational Intelligence and AI in Games, 1998, 1–1. http://doi.org/10.1109/TCIAIG.2017.2706745

Hoang, C., Luong, D., & Perez, K. (2017). Hearthstone Learning Agent Using Neural Networks, 1–10.

Grad, Ł. (2017). Helping AI to Play Hearthstone using Neural Networks. In M. Ganzha, L. Maciaszek, & M. Paprzycki (Eds.), Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (Vol. 11, pp. 131–134). http://doi.org/10.15439/2017F561

Janusz, A., Świechowski, M., & Tajmajer, T. (2017). Helping AI to Play Hearthstone: AAIA’17 Data Mining Challenge. Retrieved from http://arxiv.org/abs/1708.00730

Deja, D. (2017). Predicting Unpredictable Building Models Handling Non-IID Data Hearthstone Case Study, 11, 127–130. http://doi.org/10.15439/2017F563

Santos, A., Santos, P. A., & Melo, F. S. (2017). Monte Carlo Tree Search Experiments in Hearthstone. 2017 IEEE Conference on Computational Intelligence and Games (CIG), (June), 272–279. http://doi.org/10.1109/CIG.2017.8080446

Teo, H., & Wang, Y. (2016). Will our new robot overlords play Hearthstone with us ?, 1–5.

Doux, B., Gautrais, C., & Negrevergne, B. (2016). Detecting strategic moves in HearthStone matches. CEUR Workshop Proceedings, 1842.