On the Complexity of Rational Verification

TL;DR

This paper investigates the computational complexity of rational verification in multiagent systems, demonstrating that restrictions to certain logic fragments and goal types can significantly reduce complexity, with implications for practical verification tasks.

Contribution

It shows that restricting specifications to the GR(1) fragment reduces rational verification complexity, and provides new complexity bounds for mean-payoff goals and social welfare outcome computations.

Findings

Rational verification with GR(1) specifications can be polynomial time or space.

Complexity bounds are improved for mean-payoff utility goals.

Computing social welfare-constrained outcomes is PSPACE- or NP-complete.

Abstract

Rational verification refers to the problem of checking which temporal logic properties hold of a concurrent multiagent system, under the assumption that agents in the system choose strategies that form a game-theoretic equilibrium. Rational verification can be understood as a counterpart to model checking for multiagent systems, but while classical model checking can be done in polynomial time for some temporal logic specification languages such as CTL, and polynomial space with LTL specifications, rational verification is much harder: the key decision problems for rational verification are 2EXPTIME-complete with LTL specifications, even when using explicit-state system representations. Against this background, our contributions in this paper are threefold. First, we show that the complexity of rational verification can be greatly reduced by restricting specifications to GR(1), a fragment of LTL that can represent a broad and practically useful class of response properties of reactive systems. In particular, we show that for a number of relevant settings, rational verification can be done in polynomial space and even in polynomial time. Second, we provide improved complexity results for rational verification when considering players' goals given by mean-payoff utility functions; arguably the most widely used approach for quantitative objectives in concurrent and multiagent systems. Finally, we consider the problem of computing outcomes that satisfy social welfare constraints. To this end, we consider both utilitarian and egalitarian social welfare and show that computing such outcomes is either PSPACE-complete or NP-complete.