2023/06/01 by Giuseppe Spallitta, Roberto Sebastiani, Spallitta, Giuseppe +3
Computer Science · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2306.00461
openalex publication_date 2023/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A basic algorithm for enumerating disjoint propositional models (disjoint AllSAT) is based on adding blocking clauses incrementally, ruling out previously found models. On the one hand, blocking clauses have the potential to reduce the number of generated models exponentially, as they can handle partial models. On the other hand, the introduction of a large number of blocking clauses affects memory consumption and drastically slows down unit propagation. We propose a new approach that allows for enumerating disjoint partial models with no need for blocking clauses by integrating: Conflict-Driven Clause-Learning (CDCL), Chronological Backtracking (CB), and methods for shrinking models (Implicant Shrinking). Experiments clearly show the benefits of our novel approach.