Publication Details
Decentralized Planning Using Probabilistic Hyperproperties
Češka Milan, doc. RNDr., Ph.D. (DITS)
Macák Filip, Ing. (DITS)
FRANCESCO, P.
MICHELE, C.
Probabilistic Hyperproperties, Decentralized Planning, Markov Decision Processes,
Abstraction Refinement, Self-composition
Multi-agent planning under stochastic dynamics is usually formalised using
decentralized (partially observable) Markov decision processes (MDPs) and
reachability or expected reward specifications. In this paper, we propose
a different approach: we use an MDP describing how a single agent operates in an
environment and probabilistic hyperproperties to capture desired temporal
objectives for a set of decentralized agents operating in the environment. We
extend existing approaches for model checking probabilistic hyperproperties to
handle temporal formulae relating paths of different agents, thus requiring the
self-composition between multiple MDPs. Using several case studies, we
demonstrate that our approach provides a flexible and expressive framework to
broaden the specification capabilities with respect to existing planning
techniques. Additionally, we establish a close connection between a subclass of
probabilistic hyperproperties and planning for a particular type of Dec-MDPs, for
both of which we show undecidability. This lays the ground for the use of
existing decentralized planning tools in the field of probabilistic hyperproperty
verification.
@inproceedings{BUT196709,
author="ANDRIUSHCHENKO, R. and ČEŠKA, M. and MACÁK, F. and FRANCESCO, P. and MICHELE, C.",
title="Decentralized Planning Using Probabilistic Hyperproperties",
booktitle="Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems",
year="2025",
pages="1688--1697",
address="Detroit",
isbn="979-8-4007-1426-9"
}