{"type":"ScholarlyArticle","title":"Apprenticeship Scheduling: Learning to Schedule from Human Experts","authors":[{"name":"Matthew C. Gombolay","slug":"matthew-c-gombolay","given":"Matthew C.","family":"Gombolay","affiliation":""},{"name":"Reed Jensen","slug":"reed-jensen","given":"Reed","family":"Jensen","affiliation":""},{"name":"Jessica Stigile","slug":"jessica-stigile","given":"Jessica","family":"Stigile","affiliation":""},{"name":"Sung-Hyun Son","slug":"sung-hyun-son","given":"Sung-Hyun","family":"Son","affiliation":""},{"name":"Julie A. Shah","slug":"julie-a-shah","given":"Julie A.","family":"Shah","affiliation":""}],"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 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.","doi":null,"url":"https://ijcai.org/proceedings/2016/122","pdf":"https://ijcai.org/proceedings/2016/0122.pdf","markdown":"https://ijcai.org/proceedings/2016/122.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/122","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":122,"pages":{"first":826,"last":832},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}