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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F14%3A00431896" target="_blank" >RIV/67985556:_____/14:00431896 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-11433-0_35" target="_blank" >http://dx.doi.org/10.1007/978-3-319-11433-0_35</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-11433-0_35" target="_blank" >10.1007/978-3-319-11433-0_35</a>

Result language
angličtina
Original language name
An Approximate Tensor-Based Inference Method Applied to the Game of Minesweeper
Original language description
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.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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