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Conference Paper (international conference)

An Approximate Tensor-Based Inference Method Applied to the Game of Minesweeper

Vomlel Jiří, Tichavský Petr

: Probabilistic Graphical Models, p. 535-550 , Eds: van der Gaag Linda C. , Feelders Ad J.

: 7th European Workshop, PGM 2014,, (Utrecht, NL, 17.09.2014-19.09.2014)

: GA13-20012S, GA ČR, GA14-13713S, GA ČR

: Bayesian Networks, Probabilistic Inference, CP Tensor Decomposition, Symmetric Tensor Rank

: 10.1007/978-3-319-11433-0_35

: http://library.utia.cas.cz/separaty/2014/MTR/vomlel-0431896.pdf

(eng): We propose an approximate probabilistic inference method based on the CP-tensor decomposition and apply it to the well known computer game of Minesweeper. In the method we view conditional probability tables of the exactly l-out-of-k functions as tensors and approximate them by a sum of rank-one tensors. The number of the summands is min{l+1,k-l+1}, which is lower than their exact symmetric tensor rank, which is k. Accuracy of the approximation can be tuned by single scalar parameter. The computer game serves as a prototype for applications of inference mechanisms in Bayesian networks, which are not always tractable due to the dimensionality of the problem, but the tensor decomposition may significantly help.

: IN

: 10201