GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem

Mirgita Frasheri, Branko Miloradovic, Lukas Esterle, Alessandro V. Papadopoulos

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperConference articleResearch


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.

Original languageEnglish
Book seriesProceedings (IEEE Symposium Series on Computational Intelligence)
Pages (from-to)1696-1703
Number of pages8
Publication statusPublished - 2023
Event2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 - Mexico City, Mexico
Duration: 5 Dec 20238 Dec 2023


Conference2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023
CityMexico City


  • Autonomous Agents
  • Centralized Planning
  • Decentralized Planning
  • Multi-Agent Systems


Dive into the research topics of 'GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem'. Together they form a unique fingerprint.

Cite this