{"type":"ScholarlyArticle","title":"ProbAnch: a Modular Probabilistic Anchoring Framework","authors":[{"name":"Andreas Persson","slug":"andreas-persson","given":"Andreas","family":"Persson","affiliation":"Center for Applied Autonomous Sensor Systems (AASS) , Örebro University , Sweden"},{"name":"Pedro Zuidberg Dos Martires","slug":"pedro-zuidberg-dos-martires","given":"Pedro","family":"Zuidberg Dos Martires","affiliation":"Department of Computer Science and Leuven.AI , KU Leuven , Belgium"},{"name":"Luc de Raedt","slug":"luc-de-raedt","given":"Luc","family":"de Raedt","affiliation":"Center for Applied Autonomous Sensor Systems (AASS) , Örebro University , Sweden; Department of Computer Science and Leuven.AI , KU Leuven , Belgium"},{"name":"Amy Loufti","slug":"amy-loufti","given":"Amy","family":"Loufti","affiliation":""}],"abstract":"Modeling object representations derived from perceptual observations, in a way that is also semantically meaningful for humans as well as autonomous agents, is a prerequisite for joint human-agent understanding of the world. A practical approach that aims to model such representations is perceptual anchoring, which handles the problem of mapping sub-symbolic sensor data to symbols and maintains these mappings over time. In this paper, we present ProbAnch, a modular data-driven anchoring framework, whose implementation requires a variety of well-orchestrated components, including a probabilistic reasoning system.","doi":"10.24963/ijcai.2020/771","url":"https://ijcai.org/proceedings/2020/771","pdf":"https://ijcai.org/proceedings/2020/0771.pdf","markdown":"https://ijcai.org/proceedings/2020/771.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/771","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Demos","track":"Demos","number":771,"pages":{"first":5285,"last":5287},"keywords":["Uncertainty in AI: general","Computer Vision: general"],"videos":[],"references":[{"position":1,"raw":"[Coradeschi and Saffiotti, 2000] Silvia Coradeschi and Alessandro Saffiotti. 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