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

Wilken, Nils ; Stuckenschmidt, Heiner

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Document Type: Conference or workshop publication
Year of publication: 2021
Book title: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
Page range: 26-31
Conference title: CoMoRea '21: 17th Workshop on Context and Activity Modeling and Recognition
Location of the conference venue: Online
Date of the conference: 22.-26.03.2021
Place of publication: Piscataway, NJ
Publishing house: IEEE Computer Society
ISBN: 978-1-6654-4724-9 , 978-1-6654-0424-2
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
Subject: 004 Computer science, internet
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.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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