{"type":"ScholarlyArticle","title":"Online Probabilistic Goal Recognition over Nominal Models","authors":[{"name":"Ramon Fraga Pereira","slug":"ramon-fraga-pereira","given":"Ramon","family":"Fraga Pereira","affiliation":"Pontifical Catholic University of Rio Grande do Sul , Brazil"},{"name":"Mor Vered","slug":"mor-vered","given":"Mor","family":"Vered","affiliation":"Monash University , Australia"},{"name":"Felipe Meneguzzi","slug":"felipe-meneguzzi","given":"Felipe","family":"Meneguzzi","affiliation":"Pontifical Catholic University of Rio Grande do Sul , Brazil"},{"name":"Miquel Ramírez","slug":"miquel-ramirez","given":"Miquel","family":"Ramírez","affiliation":"The University of Melbourne , Australia"}],"abstract":"This paper revisits probabilistic, model-based goal recognition to study the implications of the use of nominal models to estimate the posterior probability distribution over a finite set of hypothetical goals. Existing model-based approaches rely on expert knowledge to produce symbolic descriptions of the dynamic constraints domain objects are subject to, and these are assumed to produce correct predictions. We abandon this assumption to consider the use of nominal models that are learnt from observations on transitions of systems with unknown dynamics. Leveraging existing work on the acquisition of domain models via learning for Hybrid Planning we adapt and evaluate existing goal recognition approaches to analyze how prediction errors, inherent to system dynamics identification and model learning techniques have an impact over recognition error rates.","doi":"10.24963/ijcai.2019/770","url":"https://ijcai.org/proceedings/2019/770","pdf":"https://ijcai.org/proceedings/2019/0770.pdf","markdown":"https://ijcai.org/proceedings/2019/770.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/770","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":"Planning and Scheduling","track":"Main track","number":770,"pages":{"first":5547,"last":5553},"keywords":["Planning and Scheduling: Activity and Plan Recognition","Planning and Scheduling: Temporal and Hybrid planning"],"videos":[],"references":[{"position":1,"raw":"[Amado et al., 2018] Leonardo Amado, Ramon Fraga Pereira, Joao Paulo Aires, Mauricio Magnaguagno, Roger Granada, and Felipe Meneguzzi. 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