Finite model finders represent a powerful tool for deciding problems with finite model property, such as the Bernays-Schonfinkel fragment (EPR). Further, finite model finders provide useful information for counter-satisfiable conjectures. The paper investigates several novel techniques in a finite model-finder based on the translation to SAT, referred to as MACE-style approach. The approach we propose is driven by counterexample abstraction refinement (CEGAR), which has proven to be a powerful tool in the context of quantifiers in satisfiability modulo theories (SMT) and quantified Boolean formulas (QBF).

One weakness of CEGAR-based approaches is that certain amount of luck is required in order to guess the right model because the solver always operates on incomplete information about the formula. To tackle this issue we propose to enhance the model finder with a machine learning algorithm to improve the likelihood that the right model is encountered. The implemented prototype based on the presented ideas shows highly promising results.