  Resource scheduling and coordination is an NP-hard optimization requiring an
efficient allocation of agents to a set of tasks with upper- and lower bound
temporal and resource constraints. Due to the large-scale and dynamic nature of
resource coordination in hospitals and factories, human domain experts manually
plan and adjust schedules on the fly. To perform this job, domain experts
leverage heterogeneous strategies and rules-of-thumb honed over years of
apprenticeship. What is critically needed is the ability to extract this domain
knowledge in a heterogeneous and interpretable apprenticeship learning
framework to scale beyond the power of a single human expert, a necessity in
safety-critical domains. We propose a personalized and interpretable
apprenticeship scheduling algorithm that infers an interpretable representation
of all human task demonstrators by extracting decision-making criteria via an
inferred, personalized embedding non-parametric in the number of demonstrator
types. We achieve near-perfect LfD accuracy in synthetic domains and 88.22\%
accuracy on a planning domain with real-world, outperforming baselines.
Finally, our user study showed our methodology produces more interpretable and
easier-to-use models than neural networks ($p < 0.05$).
