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GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem
Aarhus University, Digit, Dep. of Elect. and Comp. Eng., Denmark.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-9051-929x
Aarhus University, Digit, Dep. of Elect. and Comp. Eng., Denmark.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1364-8127
2023 (English)In: IEEE Symposium Series on Computational Intelligence, SSCI, IEEE, 2023, p. 1696-1703Conference paper, Published paper (Refereed)
Abstract [en]

Multi-agent systems can be prone to failures during the execution of a mission, depending on different circumstances, such as the harshness of the environment they are deployed in. As a result, initially devised plans for completing a mission may no longer be feasible, and a re-planning process needs to take place to re-allocate any pending tasks. There are two main approaches to solve the re-planning problem (i) global re-planning techniques using a centralized planner that will redo the task allocation with the updated world state and (ii) decentralized approaches that will focus on the local plan reparation, i.e., the re-allocation of those tasks initially assigned to the failed robots, better suited to a dynamic environment and less computationally expensive. In this paper, we propose a hybrid approach, named GLocal, that combines both strategies to exploit the benefits of both, while limiting their respective drawbacks. GLocal was compared to a planner-only, and an agent-only approach, under different conditions. We show that GLocal produces shorter mission make-spans as the number of tasks and failed agents increases, while also balancing the tradeoff between the number of messages exchanged and the number of requests to the planner.

Place, publisher, year, edition, pages
IEEE, 2023. p. 1696-1703
Keywords [en]
Autonomous Agents, Centralized Planning, Decentralized Planning, Multi-Agent Systems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-65793DOI: 10.1109/SSCI52147.2023.10371893Scopus ID: 2-s2.0-85182927382ISBN: 9781665430654 (print)OAI: oai:DiVA.org:mdh-65793DiVA, id: diva2:1833071
Conference
2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023, Mexico City, Mexico, 5-8 December, 2023
Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-01-31Bibliographically approved

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Miloradović, BrankoPapadopoulos, Alessandro

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