Combining symbolic and statistical knowledge for goal recognition in smart home environments

DOI:	https://doi.org/10.1109/PerComWorkshops51409.2021.9431145
URL:	https://ieeexplore.ieee.org/abstract/document/9431...
Weitere URL:	https://www.computer.org/csdl/proceedings-article/...
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2021
Buchtitel:	2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
Seitenbereich:	26-31
Veranstaltungstitel:	CoMoRea '21: 17th Workshop on Context and Activity Modeling and Recognition
Veranstaltungsort:	Online
Veranstaltungsdatum:	22.-26.03.2021
Ort der Veröffentlichung:	Piscataway, NJ
Verlag:	IEEE Computer Society
ISBN:	978-1-6654-4724-9 , 978-1-6654-0424-2
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-) Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
Fachgebiet:	004 Informatik
Abstract:	An important feature of pervasive, intelligent systems in smart home environments is the ability to dynamically adapt to the current needs of their inhabitants. Hence, it is critical for such systems to be able to recognize the current goals and needs of the inhabitants on the basis of observed past and current actions. This work addresses the problem of goal recognition in smart home environments. We investigate whether approaches for the plan recognition problem, which is a long standing research area in the Artificial Intelligence community, can also be applied to the goal recognition problem in smart home environments. Therefore, we evaluate the application of a well-known symbolic plan recognition approach, which is based on classical planning methods, and propose to extend this approach through additional statistical knowledge to overcome some identified shortcomings of the planning-based approach. We show that the planning-based plan recognition approach indeed can be used to solve the goal recognition problem in smart home environments and show that the proposed extension outperforms the original approach as well as purely statistical goal recognition methods.