Decentralized Planning Using Probabilistic Hyperproperties

• URL: http://arxiv.org/abs/2502.13621v1
• Date: Wed, 19 Feb 2025 10:59:02 GMT
• Title: Decentralized Planning Using Probabilistic Hyperproperties
• Authors: Francesco Pontiggia, Filip Macák, Roman Andriushchenko, Michele Chiari, Milan Češka,
• Abstract summary: We use an MDP describing how a single agent operates in an environment and probabilistic hyperproperties to capture desired temporal objectives. This lays the ground for the use of existing decentralized planning tools in the field of probabilistic hyperproperty verification.
• Score: 0.16777183511743468
• License:
• Abstract: 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.

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