Task allocation in heterogeneous multi-agent teams often requires reasoning about multi-dimensional agent traits (i.e., capabilities) and the demands placed on them by tasks. However, existing methods tend to ignore the fact that not all traits equally contribute to a given task. Ignoring such inherent preferences or relative importance can lead to unintended sub-optimal allocations of limited agent resources that do not necessarily contribute to task success. Further, reasoning over a large number of traits can incur a hefty computational burden. To alleviate these concerns, we propose an algorithm to infer task-specific trait preferences implicit in expert demonstrations. We leverage the insight that the consistency with which an expert allocates a trait to a task across demonstrations reflects the trait's importance to that task. Inspired by findings in psychology, we account for the fact that the inherent diversity of a trait in the dataset influences the dataset's informativeness and, thereby, the extent of the inferred preference or the lack thereof. Through detailed numerical simulations and evaluations of a publicly-available soccer dataset (FIFA 20), we demonstrate that we can successfully infer implicit trait preferences and that accounting for the inferred preferences leads to more computationally efficient and effective task allocation, compared to a baseline approach that treats all traits equally.

翻译：异质多智能体团队中的任务分配通常需要推理多维智能体特质（即能力）以及任务对其施加的需求。然而，现有方法往往忽略并非所有特质对给定任务同等重要这一事实。忽视此类内在偏好或相对重要性可能导致有限的智能体资源出现意外次优分配，而这些资源并不一定有助于任务成功。此外，对大量特质进行推理会带来巨大的计算负担。为缓解这些问题，我们提出一种算法，用于推断专家示范中隐含的任务特定特质偏好。我们利用如下洞见：专家在多次示范中将某一特质分配至任务的一致性，反映了该特质对该任务的重要性。受心理学研究发现启发，我们考虑了数据集中特质的固有多样性会影响数据集的丰富性，进而影响推断偏好（或缺乏偏好）的程度。通过详细的数值模拟以及对公开足球数据集（FIFA 20）的评估，我们证明能够成功推断隐性特质偏好，并且与将所有特质同等对待的基线方法相比，考虑这些推断出的偏好能够实现更高效、更有效的任务分配。