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)
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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