Monte-Carlo Style UCT Search for Boolean Satisfiability

Previti, Alessandro; Ramanujan, Raghuram; Schaerf, Marco; Selman, Bart

doi:10.1007/978-3-642-23954-0_18

Cited by 11 publications

(8 citation statements)

References 15 publications

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“…Previti et al [160] introduce the UCTSAT class of algorithms to investigate the application of UCT approaches to the satisfiability of conjunctive normal form (CNF) problems (7.8.2). They describe the following variations:…”

Section: Uctsatmentioning

confidence: 99%

“…Previti et al [160] investigate UCT approaches to the satisfiability of conjunctive normal form (CNF) problems. They find that their UCTSAT class of algorithms do not perform well if the domain being modelled has no underlying structure, but can perform very well if the information gathered on one iteration can successfully be applied on successive visits to the same node.…”

Section: Constraint Satisfactionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Monte Carlo Tree Search Methods

Browne

Powley

Whitehouse

et al. 2012

IEEE Trans. Comput. Intell. AI Games

2,329

1,535

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Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarise the results from the key game and non-game domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.

show abstract

Section: Uctsatmentioning

confidence: 99%

Section: Constraint Satisfactionmentioning

confidence: 99%

A Survey of Monte Carlo Tree Search Methods

Browne

Powley

Whitehouse

et al. 2012

IEEE Trans. Comput. Intell. AI Games

2,329

1,535

View full text Add to dashboard Cite

show abstract

“…Adaptations of Monte Carlo tree search have also been used in other contexts than game playing. It has been used in the context of constraint satisfaction problems by Previti et al to obtain good results for structured instances of the SAT problem [17]. Satomi et al [23] adapt Monte Carlo tree search to solve large-scale quantified constraint satisfaction problems (where at least one variable is universally quantified) in real-time.…”

Section: Related Workmentioning

confidence: 99%

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

Jooken¹,

Leyman²,

Causmaecker³

et al. 2020

Preprint

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In this article, a novel approach to solve combinatorial optimization problems is proposed. This approach makes use of a heuristic algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees. By leveraging the combinatorial structure of a problem, several enhancements to the algorithm are proposed. These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domain of variables by eliminating dominated solutions and using a beam width. They are demonstrated for a specific combinatorial optimization problem: the quay crane scheduling problem with non-crossing constraints. Computational results show that the proposed algorithm is competitive with the state-of-the-art for this problem and eight new best solutions for a benchmark set of instances are found. Apart from this, the results also show evidence that the algorithm is able to learn to correct the incorrect choices of a standard heuristic, yielding an average improvement of 10.0% with respect to the objective function value of the solution.

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“…Likewise, the node reward is set to the optimal makespan of the solutions built on this node. In [25], MCTS is applied to boolean satisfiability; the node reward is set to the ratio of clauses satisfied by the current assignment, tentatively estimating how far the assignment goes toward finding a solution.…”

Section: Monte Carlo Tree Searchmentioning

confidence: 99%

“…This paper advocates the use of another ML approach, namely reinforcement learning (RL) [8], to support the CP search. Taking inspiration from earlier work [25,24,23,9], the paper contribution is to extend the Monte-Carlo Tree Search (MCTS) algorithm to control the exploration of the CP search tree.…”

Section: Introductionmentioning

confidence: 99%

Bandit-Based Search for Constraint Programming

Loth

Sebag

Hamadi

et al. 2013

Lecture Notes in Computer Science

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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.

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Monte-Carlo Style UCT Search for Boolean Satisfiability

Cited by 11 publications

References 15 publications

A Survey of Monte Carlo Tree Search Methods

A Survey of Monte Carlo Tree Search Methods

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

Bandit-Based Search for Constraint Programming

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