A Unifying Formal Approach to Importance Values in Boolean Functions

TL;DR

This paper introduces a unified framework for importance values in Boolean functions, connecting various existing notions and leveraging game theory and symbolic methods for practical computation.

Contribution

It formalizes a general class of importance value functions (IVFs) based on axioms, linking them to game-theoretic measures like Shapley and Banzhaf values, and develops computational schemes using BDDs.

Findings

Established a formal connection between IVFs and game-theoretic impact measures.

Developed practical algorithms for computing IVFs using BDDs and model counting.

Demonstrated the framework's applicability to Boolean function analysis.

Abstract

Boolean functions and their representation through logics, circuits, machine learning classifiers, or binary decision diagrams (BDDs) play a central role in the design and analysis of computing systems. Quantifying the relative impact of variables on the truth value by means of importance values can provide useful insights to steer system design and debugging. In this paper, we introduce a uniform framework for reasoning about such values, relying on a generic notion of importance value functions (IVFs). The class of IVFs is defined by axioms motivated from several notions of importance values introduced in the literature, including Ben-Or and Linial's influence and Chockler, Halpern, and Kupferman's notion of responsibility and blame. We establish a connection between IVFs and game-theoretic concepts such as Shapley and Banzhaf values, both of which measure the impact of players on outcomes in cooperative games. Exploiting BDD-based symbolic methods and projected model counting, we devise and evaluate practical computation schemes for IVFs.