|Title||Apprenticeship Scheduling: Learning to Schedule from Human Experts|
|Publication Type||Conference Proceedings|
|Year of Conference||2016|
|Authors||Gombolay, M., R. Jensen, J. Stigile, S-H. Son, and J. Shah|
|Conference Name||Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)|
Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the “single-expert, single-trainee” apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state-space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on both a synthetic data set incorporating job-shop scheduling and vehicle routing problems and a real-world data set consisting of demonstrations of experts solving a weapon-to-target assignment problem.