Robust Verification of Concurrent Stochastic Games

TL;DR

This paper introduces robust concurrent stochastic games (CSGs) and interval CSGs to handle uncertainty in transition probabilities, providing new theoretical foundations and algorithms for their verification in multi-agent systems.

Contribution

The paper presents a novel framework for robust verification of CSGs under transition uncertainty, including theoretical foundations, algorithms, and implementation in PRISM-games.

Findings

Feasibility demonstrated on large benchmarks

Algorithms for finite and infinite-horizon objectives

Verification under worst-case transition uncertainty

Abstract

Autonomous systems often operate in multi-agent settings and need to make concurrent, strategic decisions, typically in uncertain environments. Verification and control problems for these systems can be tackled with concurrent stochastic games (CSGs), but this model requires transition probabilities to be precisely specified - an unrealistic requirement in many real-world settings. We introduce *robust CSGs* and their subclass *interval CSGs* (ICSGs), which capture epistemic uncertainty about transition probabilities in CSGs. We propose a novel framework for *robust* verification of these models under worst-case assumptions about transition uncertainty. Specifically, we develop the underlying theoretical foundations and efficient algorithms, for finite- and infinite-horizon objectives in both zero-sum and nonzero-sum settings, the latter based on (social-welfare optimal) Nash equilibria. We build an implementation in the PRISM-games model checker and demonstrate the feasibility of robust verification of ICSGs across a selection of large benchmarks.