Pareto Curves for Probabilistic Model Checking

TL;DR

This paper introduces a new method for multi-objective probabilistic model checking that efficiently generates Pareto curves, improving scalability and visualization for stochastic system verification.

Contribution

It proposes a novel approximation approach for Pareto curves that overcomes linear programming limitations, enabling analysis of larger systems and better visualization.

Findings

Significant efficiency improvements demonstrated on benchmarks

Enhanced visualization of Pareto curves improves result interpretation

Method enables analysis of larger, more complex systems

Abstract

Multi-objective probabilistic model checking provides a way to verify several, possibly conflicting, quantitative properties of a stochastic system. It has useful applications in controller synthesis and compositional probabilistic verification. However, existing methods are based on linear programming, which limits the scale of systems that can be analysed and makes verification of time-bounded properties very difficult. We present a novel approach that addresses both of these shortcomings, based on the generation of successive approximations of the Pareto curve for a multi-objective model checking problem. We illustrate dramatic improvements in efficiency on a large set of benchmarks and show how the ability to visualise Pareto curves significantly enhances the quality of results obtained from current probabilistic verification tools.