Symbolic Algorithms for Graphs and Markov Decision Processes with Fairness Objectives

TL;DR

This paper introduces the first sub-quadratic symbolic algorithms for verifying graphs and Markov decision processes against Streett fairness objectives, significantly improving efficiency over previous methods.

Contribution

It presents the first sub-quadratic symbolic algorithms for graphs and MDPs with Streett objectives, advancing verification techniques for reactive systems.

Findings

Sub-quadratic symbolic algorithm for graphs with Streett objectives

Sub-quadratic symbolic algorithm for MDPs with Streett objectives

Implementation shows improved performance on benchmark examples

Abstract

Given a model and a specification, the fundamental model-checking problem asks for algorithmic verification of whether the model satisfies the specification. We consider graphs and Markov decision processes (MDPs), which are fundamental models for reactive systems. One of the very basic specifications that arise in verification of reactive systems is the strong fairness (aka Streett) objective. Given different types of requests and corresponding grants, the objective requires that for each type, if the request event happens infinitely often, then the corresponding grant event must also happen infinitely often. All $\omega$-regular objectives can be expressed as Streett objectives and hence they are canonical in verification. To handle the state-space explosion, symbolic algorithms are required that operate on a succinct implicit representation of the system rather than explicitly accessing the system. While explicit algorithms for graphs and MDPs with Streett objectives have been widely studied, there has been no improvement of the basic symbolic algorithms. The worst-case numbers of symbolic steps required for the basic symbolic algorithms are as follows: quadratic for graphs and cubic for MDPs. In this work we present the first sub-quadratic symbolic algorithm for graphs with Streett objectives, and our algorithm is sub-quadratic even for MDPs. Based on our algorithmic insights we present an implementation of the new symbolic approach and show that it improves the existing approach on several academic benchmark examples.