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Combining Myopic Optimization and Tree Search: Application to MineSweeper

Michèle Sebag 1, 2 Olivier Teytaud 1, 2
2 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
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 non-reactive (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 off-the-shelf 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 brute-force 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- Confidence-Tree algorithm, both in the nodes and as a Monte-Carlo 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.
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Submitted on : Wednesday, June 27, 2012 - 9:19:06 AM
Last modification on : Thursday, November 5, 2020 - 9:06:03 AM
Long-term archiving on: : Friday, September 28, 2012 - 2:21:57 AM


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  • HAL Id : hal-00712417, version 1


Michèle Sebag, Olivier Teytaud. Combining Myopic Optimization and Tree Search: Application to MineSweeper. LION6, Learning and Intelligent Optimization, Proc. LION 6, 2012, Paris, France. pp.222-236. ⟨hal-00712417v1⟩



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