{"type":"ScholarlyArticle","title":"Online Learning of Capacity-Based Preference Models","authors":[{"name":"Margot Herin","slug":"margot-herin","given":"Margot","family":"Herin","affiliation":"Sorbonne University , CNRS , LIP6 , Paris , France"},{"name":"Patrice Perny","slug":"patrice-perny","given":"Patrice","family":"Perny","affiliation":"Sorbonne University , CNRS , LIP6 , Paris , France"},{"name":"Nataliya Sokolovska","slug":"nataliya-sokolovska","given":"Nataliya","family":"Sokolovska","affiliation":"Sorbonne University , CNRS , LCQB , Paris , France"}],"abstract":"In multicriteria decision making, sophisticated decision models often involve a non-additive set function (named capacity) to define the weights of all subsets of criteria. This makes it possible to model criteria interactions, leaving room for a diversity of attitudes in criteria aggregation. Fitting a capacity-based decision model to a given Decision Maker is a challenging problem and several batch learning methods have been proposed in the literature to derive the capacity from a database of preference examples. In this paper, we introduce an online algorithm for learning a sparse representation of the capacity, designed for decision contexts where preference examples become available sequentially. Our method based on regularized dual averaging is also well fitted to decision contexts involving a large number of preference examples or a large number of criteria. Moreover, we propose a variant making it possible to include normative constraints on the capacity (e.g., monotonicity, supermodularity) while preserving scalability, based on the alternating direction method of multipliers.","doi":"10.24963/ijcai.2024/787","url":"https://ijcai.org/proceedings/2024/787","pdf":"https://ijcai.org/proceedings/2024/0787.pdf","markdown":"https://ijcai.org/proceedings/2024/787.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/787","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":"Uncertainty in AI","track":"Main Track","number":787,"pages":{"first":7118,"last":7126},"keywords":["Machine Learning: ML: Learning preferences or rankings","Machine Learning: ML: Online learning","Uncertainty in AI: UAI: Decision and utility theory"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal and Fallah Tehrani, 2019] Manish Aggarwal and Ali Fallah Tehrani. 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