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Optimistic Heuristics for MineSweeper

Olivier Buffet 1 Chang-Shing Lee 2 Woanting Lin 3 Olivier Teytaud 4, 5, 6
1 MAIA - Autonomous intelligent machine
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
2 OASE
Institute of CSIE - Institute of Computer Science and Information Engineering [Taiwan]
3 NUTN - National University of Tainan
4 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
5 LRI - Laboratoire de Recherche en Informatique
6 Institut Montefiore - Department of Electrical Engineering and Computer Science
Abstract : We present a combination of Upper Con dence Tree (UCT) and domain speci c solvers, aimed at improving the behavior of UCT for long term aspects of a problem. Results improve the state of the art, combining top performance on small boards (where UCT is the state of the art) and on big boards (where variants of CSP rule).
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https://hal.inria.fr/hal-00750577
Contributor : Olivier Teytaud <>
Submitted on : Tuesday, February 19, 2013 - 5:00:33 PM
Last modification on : Thursday, June 17, 2021 - 3:50:33 AM
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CNRS | INRIA | INSMI | UMR8623 | UNIV-LORRAINE | LRI-AO | LORIA-AIS | TDS-MACS | LORIA | EC-PARIS

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Olivier Buffet, Chang-Shing Lee, Woanting Lin, Olivier Teytaud. Optimistic Heuristics for MineSweeper. ICS - International Computer Symposium - 2012, Dec 2012, Hualien, Taiwan. pp.199-207, ⟨10.1007/978-3-642-35452-6_22⟩. ⟨hal-00750577v2⟩

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