Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F559

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 135–138 (2017)

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Abstract. In this paper, an approach to evaluating game states of a collectible card game Hearthstone is described. A deep neural network is employed to predict the probability of winning associated with a given game state. Encoding the game state as an input vector is based on another neural network, an autoencoder with a sparsity-inducing loss. The autoencoder encodes minion information in a sparse-like fashion so that it can be efficiently aggregated. Additionally, the model is regularized by decorrelation of hidden layer neuron activations, a concept derived from an existing regularizing method DeCov. The approach was developed for AAIA'17 data mining competition``Helping AI to play Hearthstone'' and achieved 5th place out of 188 submissions.

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