{"type":"ScholarlyArticle","title":"Contract Scheduling with Distributional and Multiple Advice","authors":[{"name":"Spyros Angelopoulos","slug":"spyros-angelopoulos","given":"Spyros","family":"Angelopoulos","affiliation":"LIP6 , Sorbonne University; CNRS"},{"name":"Marcin Bienkowski","slug":"marcin-bienkowski","given":"Marcin","family":"Bienkowski","affiliation":"University of Wroclaw"},{"name":"Christoph Dürr","slug":"christoph-durr","given":"Christoph","family":"Dürr","affiliation":"LIP6 , Sorbonne University; CNRS"},{"name":"Bertrand Simon","slug":"bertrand-simon","given":"Bertrand","family":"Simon","affiliation":"CNRS; IN2P3 Computing Center"}],"abstract":"Contract scheduling is a widely studied framework for designing real-time systems with interruptible capabilities. Previous work has showed that a prediction on the interruption time can help improve the performance of contract-based systems, however it has relied on a single prediction that is provided by a deterministic oracle. In this work, we introduce and study more general and realistic learning-augmented settings in which the prediction is in the form of a probability distribution, or it is given as a set of multiple possible interruption times. For both prediction settings, we design and analyze schedules which perform optimally if the prediction is accurate, while simultaneously guaranteeing the best worst-case performance if the prediction is adversarial. We also provide evidence that the resulting system is robust to prediction errors in the distributional setting. Last, we present an experimental evaluation that confirms the theoretical findings, and illustrates the performance improvements that can be attained in practice.","doi":"10.24963/ijcai.2024/404","url":"https://ijcai.org/proceedings/2024/404","pdf":"https://ijcai.org/proceedings/2024/0404.pdf","markdown":"https://ijcai.org/proceedings/2024/404.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/404","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":404,"pages":{"first":3652,"last":3660},"keywords":["Uncertainty in AI: UAI: Sequential decision making","Planning and Scheduling: PS: Scheduling","Machine Learning: ML: Optimization","Planning and Scheduling: PS: Learning in planning and scheduling"],"videos":[],"references":[{"position":1,"raw":"[Almanza et al., 2021] Matteo Almanza, Flavio Chierichetti, Silvio Lattanzi, Alessandro Panconesi, and Giuseppe Re. 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