Good learning and implicit model enumeration

Abstract

A large number of practical applications rely on effective algorithms for propositional model enumeration and counting. Examples include knowledge compilation, model checking and hybrid solvers. Besides practical applications, the problem of counting propositional models is of key relevancy in computational complexity. In recent years a number of algorithms have been proposed for propositional model enumeration. This paper surveys algorithms for model enumeration, and proposes optimizations to existing algorithms, namely through the learning and simplification of goods. Moreover, the paper also addresses open topics in model counting related with good learning. Experimental results indicate that the proposed techniques are effective for model enumeration