Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

A Neural Network Approach to Hearthstone Win Rate Prediction

DOI: http://dx.doi.org/10.15439/2018F365

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 185188 ()

Full text

Abstract. This paper describes a solution to the AAIA'18 data mining challenge, which concerns prediction of win rates for decks in Hearthstone collectible card game. A neural network model assigning win rate to decks is learned based on maximisation of log probability of observed match results. A representation of data is based on a second network, which performs the role of a dual-task encoder. Two tasks learned by the encoding networks are encoding decks in such a way that the full deck can be reconstructed, and encoding individual cards so that their specific properties can be decoded. Shared representation for these tasks allows the knowledge of individual cards to be taken into account.


  1. https://hearthpwn.com
  2. https://hearthstonejson.com/docs/cards.html
  3. Coates, Adam, Andrew Ng, and Honglak Lee. "An analysis of single-layer networks in unsupervised feature learning." Proceedings of the fourteenth international conference on artificial intelligence and statistics. 2011.
  4. Deng, Li. "A tutorial survey of architectures, algorithms, and applications for deep learning." APSIPA Transactions on Signal and Information Processing 3 (2014).
  5. Zeiler, Matthew D. "ADADELTA: an Adaptive Learning Rate Method." Computing Research Repository, 2012