论文标题
由多代理团队中的负载管理分配的任务分配
Task Allocation with Load Management in Multi-Agent Teams
论文作者
论文摘要
在从同质机器人群到异质人自动团队的多机构团队的运营中，可能会发生意外的事件。虽然对多代理任务分配问题的操作效率是主要目标，但必须使决策框架足够聪明，可以用有限的资源管理意外的任务负载。否则，操作效率将大大落下，而过载的代理人面临不可预见的风险。在这项工作中，我们为多机构团队提供了一个决策框架，以通过分散的强化学习来考虑负载管理，以学习任务分配，并避免了闲置和不必要的资源使用情况。我们说明了负载管理对团队绩效的影响，并在示例场景中探索了代理行为。此外，在处理潜在的超负荷情况时，开发了一种衡量协作中的代理重要性的衡量标准。
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 multi-agent 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.
