  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.
