Dynamic exploration of multi-agent systems with timed periodic tasks

TL;DR

This paper introduces MAPTs, a formal model for multi-agent systems with timed periodic tasks, enabling efficient dynamic state space exploration and comparison with existing methods in autonomous vehicle scenarios.

Contribution

The paper presents MAPTs, a novel formalism that accelerates state space exploration through layered structures and heuristics, improving reachability analysis in multi-agent timed systems.

Findings

MAPTs enable faster state space exploration.

MAPTs demonstrate higher expressivity and abstraction.

Application to autonomous vehicles shows improved efficiency.

Abstract

We formalise and study multi-agent timed models MAPTs (Multi-Agent with timed Periodic Tasks), where each agent is associated to a regular timed schema upon which all possibles actions of the agent rely. MAPTs allow for an accelerated semantics and a layered structure of the state space, so that it is possible to explore the latter dynamically and use heuristics to greatly reduce the computation time needed to address reachability problems. We apply MAPTs to explore state spaces of autonomous vehicles and compare it with other approaches in terms of expressivity, abstraction level and computation time.