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Bandit-Based Search for Constraint Programming

  • Manuel Loth
  • Michèle Sebag
  • Youssef Hamadi
  • Marc Schoenauer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8124)

Abstract

Constraint Programming (CP) solvers classically explore the solution space using tree-search based heuristics. Monte-Carlo Tree Search (MCTS), aimed at optimal sequential decision making under uncertainty, gradually grows a search tree to explore the most promising regions according to a specified reward function. At the crossroad of CP and MCTS, this paper presents the Bandit Search for Constraint Programming (BaSCoP) algorithm, adapting MCTS to the specifics of the CP search. This contribution relies on i) a generic reward function suited to CP and compatible with a multiple restart strategy; ii) the use of depth-first search as roll-out procedure in MCTS. BaSCoP, on the top of the Gecode constraint solver, is shown to significantly improve on depth-first search on some CP benchmark suites, demonstrating its relevance as a generic yet robust CP search method.

Keywords

adaptive search value selection bandit UCB MCTS 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Manuel Loth
    • 1
  • Michèle Sebag
    • 2
  • Youssef Hamadi
    • 3
  • Marc Schoenauer
    • 2
  1. 1.Microsoft Research – INRIA joint centrePalaiseauFrance
  2. 2.TAO, INRIA − CNRS − LRIUniversité Paris-SudOrsayFrance
  3. 3.Microsoft ResearchCambridgeUnited Kingdom

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