Projected Model Counting: Beyond Independent Support

TL;DR

This paper introduces the concept of upper bound support (UBS) for projected model counting, demonstrating that projecting on UBS can provide tighter bounds and improved efficiency over traditional independent support methods.

Contribution

The paper proposes the novel idea of using upper bound support (UBS) for projected model counting, which can be exponentially smaller and more effective than independent support.

Findings

UBS can provide tighter upper bounds on projected model counts.

UBS-based counting outperforms independent support methods in efficiency.

UBS-based methods solve more complex instances than state-of-the-art approaches.

Abstract

The past decade has witnessed a surge of interest in practical techniques for projected model counting. Despite significant advancements, however, performance scaling remains the Achilles' heel of this field. A key idea used in modern counters is to count models projected on an \emph{independent support} that is often a small subset of the projection set, i.e. original set of variables on which we wanted to project. While this idea has been effective in scaling performance, the question of whether it can benefit to count models projected on variables beyond the projection set, has not been explored. In this paper, we study this question and show that contrary to intuition, it can be beneficial to project on variables beyond the projection set. In applications such as verification of binarized neural networks, quantification of information flow, reliability of power grids etc., a good upper bound of the projected model count often suffices. We show that in several such cases, we can identify a set of variables, called upper bound support (UBS), that is not necessarily a subset of the projection set, and yet counting models projected on UBS guarantees an upper bound of the true projected model count. Theoretically, a UBS can be exponentially smaller than the smallest independent support. Our experiments show that even otherwise, UBS-based projected counting can be more efficient than independent support-based projected counting, while yielding bounds of very high quality. Based on extensive experiments, we find that UBS-based projected counting can solve many problem instances that are beyond the reach of a state-of-the-art independent support-based projected model counter.