{"type":"ScholarlyArticle","title":"How to Tame Your Anticipatory Algorithm","authors":[{"name":"Allegra De Filippo","slug":"allegra-de-filippo","given":"Allegra","family":"De Filippo","affiliation":"Department of Computer Science and Engineering , University of Bologna"},{"name":"Michele Lombardi","slug":"michele-lombardi","given":"Michele","family":"Lombardi","affiliation":"Department of Computer Science and Engineering , University of Bologna"},{"name":"Michela Milano","slug":"michela-milano","given":"Michela","family":"Milano","affiliation":"Department of Computer Science and Engineering , University of Bologna"}],"abstract":"Sampling-based anticipatory algorithms can be very effective at solving online optimization problems under uncertainty, but their computational cost may be prohibitive in some cases. Given an arbitrary anticipatory algorithm, we present three methods that allow to retain its solution quality at a fraction of the online computational cost, via a substantial degree of offline preparation. Our approaches are obtained by combining: 1) a simple technique to identify likely future outcomes based on past observations; 2) the (expensive) offline computation of a \"contingency table\"; and 3) an efficient solution-fixing heuristic. We ground our techniques on two case studies: an energy management system with uncertain renewable generation and load demand, and a traveling salesman problem with uncertain travel times. In both cases, our techniques achieve high solution quality, while substantially reducing the online computation time.","doi":"10.24963/ijcai.2019/150","url":"https://ijcai.org/proceedings/2019/150","pdf":"https://ijcai.org/proceedings/2019/0150.pdf","markdown":"https://ijcai.org/proceedings/2019/150.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/150","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Constraints and SAT","track":"Main track","number":150,"pages":{"first":1071,"last":1077},"keywords":["Heuristic Search and Game Playing: Combinatorial Search and Optimisation","Uncertainty in AI: Uncertainty in AI","Constraints and SAT: Constraint Satisfaction"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2015] Hao Bai, Shihong Miao, Xiaohong Ran, and Chang Ye. 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