Top-Down Knowledge Compilation for Counting Modulo Theories
Abstract
Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF). Translating an arbitrary formula into a representation that allows inference tasks, such as counting, to be performed efficiently, is called knowledge compilation. Top-down knowledge compilation is a state-of-the-art technique for solving #SAT problems that leverages the traces of exhaustive DPLL search to obtain d-DNNF representations. While knowledge compilation is well studied for propositional approaches, knowledge compilation for the (quantifier free) counting modulo theory setting (#SMT) has been studied to a much lesser degree. In this paper, we discuss compilation strategies for #SMT. We specifically advocate for a top-down compiler based on the traces of exhaustive DPLL(T) search.
Keywords
Cite
@article{arxiv.2306.04541,
title = {Top-Down Knowledge Compilation for Counting Modulo Theories},
author = {Vincent Derkinderen and Pedro Zuidberg Dos Martires and Samuel Kolb and Paolo Morettin},
journal= {arXiv preprint arXiv:2306.04541},
year = {2023}
}
Comments
9 pages; submitted to Workshop on Counting and Sampling 2023 at SAT2023