Task Allocation with Load Management in Multi-Agent Teams

Haochen Wu, Amin Ghadami, Alparslan Emrah Bayrak, Jonathon M. Smereka, Bogdan I. Epureanu

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Scopus citations

    Abstract

    In operations of multi-agent teams ranging from homogeneous robot swarms to heterogeneous human-autonomy teams, unexpected events might occur. While efficiency of operation for multi-agent task allocation problems is the primary objective, it is essential that the decision-making framework is intelligent enough to manage unexpected task load with limited resources. Otherwise, operation effectiveness would drastically plummet with overloaded agents facing unforeseen risks. In this work, we present a decision-making framework for multiagent teams to learn task allocation with the consideration of load management through decentralized reinforcement learning, where idling is encouraged and unnecessary resource usage is avoided. We illustrate the effect of load management on team performance and explore agent behaviors in example scenarios. Furthermore, a measure of agent importance in collaboration is developed to infer team resilience when facing handling potential overload situations.

    Original languageEnglish
    Title of host publication2022 IEEE International Conference on Robotics and Automation, ICRA 2022
    Pages8823-8830
    Number of pages8
    ISBN (Electronic)9781728196817
    DOIs
    StatePublished - 2022
    Event39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States
    Duration: 23 May 202227 May 2022

    Publication series

    NameProceedings - IEEE International Conference on Robotics and Automation
    ISSN (Print)1050-4729

    Conference

    Conference39th IEEE International Conference on Robotics and Automation, ICRA 2022
    Country/TerritoryUnited States
    CityPhiladelphia
    Period23/05/2227/05/22

    Keywords

    • AI-Based Methods
    • Cooperating Robots
    • Multi-Robot Systems
    • Reinforcement Learning
    • Task Planning

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