Apprenticeship scheduling: Learning to schedule from human experts
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Gombolay_IJCAI_2016.pdf
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Author(s)
Gombolay, Matthew C.
Jensen, Reed E.
Stigile, Jessica L.
Son, Sung-Hyun
Shah, Julie A
Date Issued
July 2016
Journal
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)
Publisher
AAAI Press / International Joint Conferences on Artificial Intelligence
Citation
Gombolay, Matthew, Reed Jensen, Jessica Stigile, Sung-Hyun Son and Julie Shah. "Apprenticeship scheduling: Learning to schedule from human experts." In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), New York, New York, July 09-15, 2016.
Version
Author's final manuscript
Abstract
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 "singleexpert, 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 jobshop scheduling and vehicle routing problems and a real-world data set consisting of demonstrations of experts solving a weapon-to-target assignment problem.
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://dx.doi.org/
