ADDMC: Weighted Model Counting with Algebraic Decision Diagrams - Dudek, Jeffrey; Phan, Vu; Vardi, Moshe (Proceedings of the AAAI Conference on Artificial Intelligence, 2020)
We present an algorithm to compute exact literal-weighted model counts of Boolean formulas in Conjunctive Normal Form. Our algorithm employs dynamic programming and uses Algebraic Decision Diagrams as the main data structure. We implement this technique in ADDMC, a new model counter. We empirically evaluate various heuristics that can be used with ADDMC. We then compare ADDMC to four state-of-the-art weighted model counters (Cachet, c2d, d4, and miniC2D) on 1914 standard model counting benchmarks and show that ADDMC significantly improves the virtual best solver.