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

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Probabilistic Graphical Models (PGM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8754))

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Abstract

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.

This work was supported by the Czech Science Foundation through projects 13–20012S and 14–13713S.

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Author information

Authors and Affiliations

  1. Institute of Information Theory and Automation of the AS CR, Pod vodárenskou věží 4, Prague, 182 08, Czech Republic

    Jiří Vomlel & Petr Tichavský

Authors
  1. Jiří Vomlel
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  2. Petr Tichavský
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Editor information

Editors and Affiliations

  1. Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht, The Netherlands

    Linda C. van der Gaag

  2. Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht,, The Netherlands

    Ad J. Feelders

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Vomlel, J., Tichavský, P. (2014). An Approximate Tensor-Based Inference Method Applied to the Game of Minesweeper. In: van der Gaag, L.C., Feelders, A.J. (eds) Probabilistic Graphical Models. PGM 2014. Lecture Notes in Computer Science(), vol 8754. Springer, Cham. https://doi.org/10.1007/978-3-319-11433-0_35

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  • DOI: https://doi.org/10.1007/978-3-319-11433-0_35

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11432-3

  • Online ISBN: 978-3-319-11433-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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