Ing. Filip Beskyd

Stať ve sborníku

Katedra aplikované matematiky

Boolean satisfiability (SAT) solvers are essential tools for many domains in computer science and engineering. Modern complete search-based SAT solvers represent a universal problem solving tool which often provide higher efficiency than ad-hoc direct solving approaches. Over the course of at least two decades of SAT related research, many variable and value selection heuristics were devised. Heuristics can usually be tuned by single or multiple numerical parameters prior to executing the search process over the concrete SAT instance. In this paper we present a machine learning approach that predicts the parameters of heuristic from the underlying structure of the input SAT instance.