Information Measures in Detecting and Recognizing Symmetries

TL;DR

This paper introduces an information-theoretic approach combined with efficient estimation techniques on Binary Decision Diagrams to detect and recognize symmetries in large-scale Boolean functions, enhancing symmetry analysis capabilities.

Contribution

It proposes a novel method that leverages information measures and BDD-based estimations for symmetry detection in Boolean functions, improving scalability and accuracy.

Findings

Effective detection of symmetries in large Boolean functions

Enhanced estimation techniques for information measures on BDDs

Potential for improved symmetry recognition in complex systems

Abstract

This paper presents a method to detect and recognize symmetries in Boolean functions. The idea is to use information theoretic measures of Boolean functions to detect sub-space of possible symmetric variables. Coupled with the new techniques of efficient estimations of information measures on Binary Decision Diagrams (BDDs) we obtain promised results in symmetries detection for large-scale functions.