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Recent work proposed the UCTMAXSAT algorithm to address MaximumSatisfiability Problems (MaxSAT) and shown improved performance over pureStochastic Local Search algorithms (SLS). UCTMAXSAT is based on Monte CarloTree Search but it uses SLS instead of purely random playouts. In this work, weintroduce two algorithmic variations over UCTMAXSAT. We carry an empiricalanalysis on MaxSAT benchmarks from recent competitions and establish that bothideas lead to performance improvements. First, a nesting of the tree searchinspired by the Nested Monte Carlo Search algorithm is effective on mostinstance types in the benchmark. Second, we observe that using a static fliplimit in SLS, the ideal budget depends heavily on the instance size and wepropose to set it dynamically. We show that it is a robust way to achievecomparable performance on a variety of instances without requiring additionaltuning.

Towards Tackling MaxSAT by Combining Nested Monte Carlo with Local Search