Non-Deterministic Planning for Hyperproperty Verification

Raven Beutner, Bernd Finkbeiner

Non-deterministic planning aims to find a policy that achieves a given objective in an environment where actions have uncertain effects, and the agent — potentially — only observes parts of the current state. Hyperproperties are properties that relate multiple paths of a system and can, e.g., capture security and information-flow policies. Popular logics for expressing temporal hyperproperties — such as HyperLTL — extend LTL by offering selective quantification over executions of a system. In this paper, we show that planning offers a powerful intermediate language for the automated verification of hyperproperties. Concretely, we present an algorithm that, given a HyperLTL verification problem, constructs a non-deterministic multi-agent planning instance (in the form of a QDec-POMDP) that, when admitting a plan, implies the satisfaction of the verification problem. We show that for large fragments of HyperLTL, the resulting planning instance corresponds to a classical, FOND, or POND planning problem. We implement our encoding in a prototype verification tool and report on encouraging experimental results using off-the-shelf FOND planners.

34th International Conference on Automated Planning and Scheduling (ICAPS 2024).