You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

Probabilistic Mission Planning and Analysis for Multi-agent Systems


Publication Type:

Conference/Workshop Paper


9th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation


Mission planning is one of the crucial problems in the design of autonomous Multi-Agent Systems (MAS), requiring the agents to calculate collision-free paths and efficiently schedule their tasks. The complexity of this problem greatly increases when the number of agents grows, as well as timing requirements and stochastic behavior of agents are considered. In this paper, we propose a novel method that integrates statistical model checking and reinforcement learning for mission planning within such context. Additionally, in order to synthesise mission plans that are statistically optimal, we employ hybrid automata to model the continuous movement of agents and moving obstacles, and estimate the possible delay of the agents' travelling time when facing unpredictable obstacles. We show the result of synthesising mission plans, analyze bottlenecks of the mission plans, and re-plan when pedestrians suddenly appear, by modeling and verifying a real industrial use case in UPPAAL SMC.


author = {Rong Gu and Eduard Paul Enoiu and Cristina Seceleanu and Kristina Lundqvist},
title = {Probabilistic Mission Planning and Analysis for Multi-agent Systems},
month = {October},
year = {2020},
booktitle = {9th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation},
url = {}