TY - GEN
T1 - Task Allocation with Load Management in Multi-Agent Teams
AU - Wu, Haochen
AU - Ghadami, Amin
AU - Bayrak, Alparslan Emrah
AU - Smereka, Jonathon M.
AU - Epureanu, Bogdan I.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - AI-Based Methods
KW - Cooperating Robots
KW - Multi-Robot Systems
KW - Reinforcement Learning
KW - Task Planning
UR - http://www.scopus.com/inward/record.url?scp=85136332973&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136332973&partnerID=8YFLogxK
U2 - 10.1109/ICRA46639.2022.9811374
DO - 10.1109/ICRA46639.2022.9811374
M3 - Conference contribution
AN - SCOPUS:85136332973
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 8823
EP - 8830
BT - 2022 IEEE International Conference on Robotics and Automation, ICRA 2022
T2 - 39th IEEE International Conference on Robotics and Automation, ICRA 2022
Y2 - 23 May 2022 through 27 May 2022
ER -