LOAINov 6, 2023

Using Symmetries to Lift Satisfiability Checking

arXiv:2311.03424v2h-index: 37
AI Analysis

This work addresses efficiency issues in satisfiability problems for domains like configuration and software verification, though it is incremental as it builds on existing symmetry reduction techniques.

The paper tackles the problem of improving performance in satisfiability checking by using symmetries to compress structures without losing information, resulting in large speedups for generative configuration problems.

We analyze how symmetries can be used to compress structures (also known as interpretations) onto a smaller domain without loss of information. This analysis suggests the possibility to solve satisfiability problems in the compressed domain for better performance. Thus, we propose a 2-step novel method: (i) the sentence to be satisfied is automatically translated into an equisatisfiable sentence over a ``lifted'' vocabulary that allows domain compression; (ii) satisfiability of the lifted sentence is checked by growing the (initially unknown) compressed domain until a satisfying structure is found. The key issue is to ensure that this satisfying structure can always be expanded into an uncompressed structure that satisfies the original sentence to be satisfied. We present an adequate translation for sentences in typed first-order logic extended with aggregates. Our experimental evaluation shows large speedups for generative configuration problems. The method also has applications in the verification of software operating on complex data structures. Our results justify further research in automatic translation of sentences for symmetry reduction.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes