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 http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • 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 webmaster@ide.mdh.se

Strategy Synthesis and Compression for Multi-Agent Autonomous Systems: A Correctness-Guaranteed Approach

Fulltext:


Publication Type:

Report - MRTC

Publisher:

Mälardalen Real-Time Research Centre, Mälardalen University

ISRN:

MDH-MRTC-342/2022-1-SE


Abstract

Planning is a critical function of multi-agent autonomous systems, which includes path finding and task scheduling. Exhaustive search-based methods such as model checking and algorithmic game theory can solve simple instances of multi-agent planning. However, these methods suffer from the state-space explosion when the number of agents is large. Learning-based methods can alleviate this problem but lack a guarantee of the correctness of the results. In this paper, we introduce MoCReL, a new version of our previously proposed method that combines model checking with reinforcement learning in solving the planning problem. The approach takes advantage of reinforcement learning to synthesize path plans and task schedules for large numbers of autonomous agents, and of model checking to verify the correctness of the synthesized strategies. Further, MoCReL can compress large strategies into smaller ones that have down to 0.05% of the original sizes, while preserving their correctness, which we show in this paper. MoCReL is integrated into a new version of UPPAAL Stratego that supports calling external libraries when running learning and verification of timed games models.

Bibtex

@techreport{Gu6415,
author = {Rong Gu and Peter Jensen and Cristina Seceleanu and Eduard Paul Enoiu and Kristina Lundqvist},
title = {Strategy Synthesis and Compression for Multi-Agent Autonomous Systems: A Correctness-Guaranteed Approach},
month = {April},
year = {2022},
publisher = {M{\"a}lardalen Real-Time Research Centre, M{\"a}lardalen University},
url = {http://www.es.mdu.se/publications/6415-}
}