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Erschienen in:
2014 | OriginalPaper | Buchkapitel
An Approximate Tensor-Based Inference Method Applied to the Game of Minesweeper
verfasst von : Jiří Vomlel, Petr Tichavský
Erschienen in: Probabilistic Graphical Models
Verlag: Springer International Publishing
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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.