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

Inferring Implicit Trait Preferences for Task Allocation in Heterogeneous Teams