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Problem Addressed

Coordinating agents to complete a set of tasks with temporal and resource constraints is a challenging problem requiring human domain experts to employ knowledge paradigms learned through years of apprenticeship. A process to manually codify 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. The Inventors have developed a new approach to capture domain-expert heuristics through a pairwise ranking formulation that accurately learns multifaceted heuristics on both synthetic and real world data sets.