Cooperative Task Execution in Multi-Agent Systems
CoRR(2024)
摘要
We propose a multi-agent system that enables groups of agents to collaborate
and work autonomously to execute tasks. Groups can work in a decentralized
manner and can adapt to dynamic changes in the environment. Groups of agents
solve assigned tasks by exploring the solution space cooperatively based on the
highest reward first. The tasks have a dependency structure associated with
them. We rigorously evaluated the performance of the system and the individual
group performance using centralized and decentralized control approaches for
task distribution. Based on the results, the centralized approach is more
efficient for systems with a less-dependent system G_18, while the
decentralized approach performs better for systems with a highly-dependent
system G_40. We also evaluated task allocation to groups that do not have
interdependence. Our findings reveal that there was significantly less
difference in the number of tasks allocated to each group in a less-dependent
system than in a highly-dependent one. The experimental results showed that a
large number of small-size cooperative groups of agents unequivocally improved
the system's performance compared to a small number of large-size cooperative
groups of agents. Therefore, it is essential to identify the optimal group size
for a system to enhance its performance.
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