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 depthfirst 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.
For example, the Monte-Carlo tree search based method in REF tries to expand the most promising nodes with online learning.
Bandit-based Search for Constraint Programming
CP
Computer Science