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Best results so far for minesweeper on small board sizes can be found in http://hal.inria.fr/hal00712417.
2 bibtex references below:
@article{10.1109/TAAI.2011.55,
author = {Adrien Couetoux and Mario Milone and Olivier Teytaud},
title = {Consistent Belief State Estimation, with Application to Mines},
journal ={Technologies and Applications of Artificial Intelligence, International Conference on},
volume = {0},
isbn = {9780769546018},
year = {2011},
pages = {280285},
doi = {http://doi.ieeecomputersociety.org/10.1109/TAAI.2011.55},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
And the UCT performances on MineSweeper on small board:
@inproceedings{sebag:hal00712417,
hal_id = {hal00712417},
url = {http://hal.inria.fr/hal00712417},
title = {{Combining Myopic Optimization and Tree Search: Application to MineSweeper}},
author = {Sebag, Mich{\`e}le and Teytaud, Olivier},
abstract = {{Abstract. Many reactive planning tasks are tackled by optimization combined with shrinking horizon at each time step: the problem is sim plified to a nonreactive (myopic) optimization problem, based on the available information at the current time step and an estimate of future behavior, then it is solved; and the simplified problem is updated at each time step thanks to new information. This is in particular suitable when fast offtheshelf components are available for the simplified problem  optimality stricto sensu is not possible, but good results are obtained at a reasonnable computational cost for highly untractable problems. As machines get more powerful, it makes sense however to go beyond the inherent limitations of this approach. Yet, a bruteforce solving of the complete problem is often impossible; we here propose a methodology for embedding a solver inside a consistent reactive planning solver. Our methodology consists in embedding the solver in an Upper ConfidenceTree algorithm, both in the nodes and as a MonteCarlo simulator. We show the mathematical consistency of the approach, and then we apply it to a classical success of the myopic approach: the MineSweeper game.}},
language = {Anglais},
affiliation = {Laboratoire de Recherche en Informatique  LRI , TAO  INRIA Saclay  Ile de France},
booktitle = {{LION6, Learning and Intelligent Optimization}},
pages = {in press (14 pages, long paper)},
address = {Paris, France},
audience = {internationale },
year = {2012},
pdf = {http://hal.inria.fr/hal00712417/PDF/mines2.pdf},
}
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