Modeling and reasoning with ProbLog: an application in recognizing complex activities


Sztyler, Timo ; Civitarese, Gabriele ; Stuckenschmidt, Heiner



DOI: https://doi.org/10.1109/PERCOMW.2018.8480299
URL: https://ieeexplore.ieee.org/document/8480299
Document Type: Conference or workshop publication
Year of publication: 2018
Book title: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 : Athens, Greece, March 19-23, 2018
Page range: 259-264
Conference title: CoMoRea '18: 14th Workshop on Context and Activity Modeling and Recognition : affiliated to IEEE PerCom 2018
Location of the conference venue: Athens, Greece
Date of the conference: 19.-23.03.2018
Place of publication: Piscataway, NJ
Publishing house: IEEE Computer Society
ISBN: 978-1-5386-3228-4 , 978-1-5386-3227-7 , 978-1-5386-3226-0
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: Smart-home activity recognition is an enabling tool for a wide range of ambient assisted living applications. The recognition of ADLs usually relies on supervised learning or knowledge-based reasoning techniques. In order to overcome the well-known limitations of those two approaches and, at the same time, to combine their strengths to improve the recognition rate, many researchers investigated Markov Logic Networks (MLNs). However, MLNs require a non-trivial effort by experts to properly model probabilities in terms of weights. In this paper, we propose a novel method based on ProbLog. ProbLog is a probabilistic extension of Prolog, which allows to explicitly define probabilistic facts and rules. With respect to MLN, the inference mode of ProbLog is based on the closed-world assumption and it has faster response times. We propose a simple and flexible ProbLog model, which we exploit to recognize complex ADLs in an online fashion. Considering a dataset with 21 subjects, our results show that our method reaches high F-measure (83%). Moreover, we also show that the response time of ProbLog is satisfying for real-time applications.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Sztyler, Timo ; Civitarese, Gabriele ; Stuckenschmidt, Heiner ORCID: 0000-0002-0209-3859 Modeling and reasoning with ProbLog: an application in recognizing complex activities. 259-264 In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 : Athens, Greece, March 19-23, 2018 (2018) Piscataway, NJ CoMoRea '18: 14th Workshop on Context and Activity Modeling and Recognition : affiliated to IEEE PerCom 2018 (Athens, Greece) [Conference or workshop publication]


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