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
Dokumenttyp: Konferenzveröffentlichung
Erscheinungsjahr: 2018
Buchtitel: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 : Athens, Greece, March 19-23, 2018
Seitenbereich: 259-264
Veranstaltungstitel: CoMoRea '18: 14th Workshop on Context and Activity Modeling and Recognition : affiliated to IEEE PerCom 2018
Veranstaltungsort: Athens, Greece
Veranstaltungsdatum: 19.-23.03.2018
Ort der Veröffentlichung: Piscataway, NJ
Verlag: IEEE Computer Society
ISBN: 978-1-5386-3228-4 , 978-1-5386-3227-7 , 978-1-5386-3226-0
Sprache der Veröffentlichung: Englisch
Einrichtung: Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Fachgebiet: 004 Informatik
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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