diff-SAT -- A Software for Sampling and Probabilistic Reasoning for SAT and Answer Set Programming
This work provides a tool for researchers and practitioners in probabilistic logic programming and satisfiability solving to perform distribution-aware sampling and probabilistic reasoning.
This paper introduces diff-SAT, a solver for SAT and Answer Set Programming that integrates probabilistic clauses, facts, and rules. It can sample an optimal multiset of satisfying assignments or answer sets by minimizing a user-defined differentiable objective function using gradient descent.
This paper describes diff-SAT, an Answer Set and SAT solver which combines regular solving with the capability to use probabilistic clauses, facts and rules, and to sample an optimal world-view (multiset of satisfying Boolean variable assignments or answer sets) subject to user-provided probabilistic constraints. The sampling process minimizes a user-defined differentiable objective function using a gradient descent based optimization method called Differentiable Satisfiability Solving ($\partial\mathrm{SAT}$) respectively Differentiable Answer Set Programming ($\partial\mathrm{ASP}$). Use cases are i.a. probabilistic logic programming (in form of Probabilistic Answer Set Programming), Probabilistic Boolean Satisfiability solving (PSAT), and distribution-aware sampling of model multisets (answer sets or Boolean interpretations).