An algorithm to evaluate quantified Boolean formulae
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
An Algorithm to Evaluate Quantified Boolean Formulae and Its Experimental Evaluation
Journal of Automated Reasoning
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Random 3-SAT and BDDs: The Plot Thickens Further
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Random 3-SAT: The Plot Thickens
Constraints
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
Deriving Information from Sampling and Diving
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Applying UCT to boolean satisfiability
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
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In this paper, we investigate the feasibility of applying algorithms based on the Uniform Confidence bounds applied to Trees [12] to the satisfiability of CNF formulas. We develop a new family of algorithms based on the idea of balancing exploitation (depth-first search) and exploration (breadth-first search), that can be combined with two different techniques to generate random playouts or with a heuristics-based evaluation function. We compare our algorithms with a DPLL-based algorithm and with WalkSAT, using the size of the tree and the number of flips as the performance measure. While our algorithms perform on par with DPLL on instances with little structure, they do quite well on structured instances where they can effectively reuse information gathered from one iteration on the next. We also discuss the pros and cons of our different algorithms and we conclude with a discussion of a number of avenues for future work.