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Modeling and reasoning with ProbLog: an application in recognizing complex activities
Sztyler, Timo
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Civitarese, Gabriele
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Stuckenschmidt, Heiner
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DOI:
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https://doi.org/10.1109/PERCOMW.2018.8480299
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URL:
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https://ieeexplore.ieee.org/document/8480299
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2018
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Buchtitel:
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2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 : Athens, Greece, March 19-23, 2018
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Seitenbereich:
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259-264
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Veranstaltungstitel:
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CoMoRea '18: 14th Workshop on Context and Activity Modeling and Recognition : affiliated to IEEE PerCom 2018
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Veranstaltungsort:
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Athens, Greece
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Veranstaltungsdatum:
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19.-23.03.2018
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Ort der Veröffentlichung:
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Piscataway, NJ
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Verlag:
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IEEE Computer Society
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ISBN:
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978-1-5386-3228-4 , 978-1-5386-3227-7 , 978-1-5386-3226-0
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Fachgebiet:
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004 Informatik
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Abstract:
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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.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Suche Autoren in
BASE:
Sztyler, Timo
;
Civitarese, Gabriele
;
Stuckenschmidt, Heiner
Google Scholar:
Sztyler, Timo
;
Civitarese, Gabriele
;
Stuckenschmidt, Heiner
ORCID:
Sztyler, Timo, Civitarese, Gabriele and Stuckenschmidt, Heiner  ORCID: https://orcid.org/0000-0002-0209-3859
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