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Using Constraint Optimization for Conflict Resolution and Detail Control in Activity Recognition

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Ambient Intelligence (AmI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7040))

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Abstract

In Ambient Assisted Living and other environments the problem is to recognize all of user activities. Due to noisy or incomplete information a naïve recognition system may report activities that are logically inconsistent with each other, e.g., the user is sleeping on the couch and at the same time is watching TV. In this work, we develop a rule-based recognition system for hierarchically-organized activities that returns only logically consistent scenarios. This is achieved by explicitly formulating conflicts as Weighted Partial MaxSAT clauses to be satisfied. The system also has the ability to adjust the desired level of detail of the scenarios returned. This is accomplished by assigning preferences to clauses of the SAT problem. The system is implemented and evaluated in a real Ambient Intelligence experimental space. It is shown to be robust to the presence of noise; the level of detail can easily be adjusted by the use of two preference parameters.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Crete, Greece

    Chrysi Filippaki, Grigoris Antoniou & Ioannis Tsamardinos

  2. Institute of Computer Science, Forth-ICS, Heraklion, Greece

    Chrysi Filippaki, Grigoris Antoniou & Ioannis Tsamardinos

Authors
  1. Chrysi Filippaki
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  2. Grigoris Antoniou
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  3. Ioannis Tsamardinos
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Editor information

Editors and Affiliations

  1. Delft University of Technology, Landbergstraat 15, 2628CE, Delft, The Netherlands

    David V. Keyson

  2. College of Information Studies, University of Maryland, College Park, 2117E Hornbake Hall, 20742-4345, Marylandm, USA

    Mary Lou Maher

  3. Smart Future Initiative, Konrad-Zuse-Str. 43, 60438, Frankfurt, Germany

    Norbert Streitz

  4. Keio University, Japan and National University of Singapore, Heng Miu Keng Terrace 02-01-01A, 119613, Singapore

    Adrian Cheok

  5. School of Computing and Mathematics, University of Ulster, Shore Road, BT37 0QB, Newtownabbey, Co. Antrim, UK

    Juan Carlos Augusto

  6. Fraunhofer-Institut für Graphische Datenverarbeitung IGD, Fraunhoferstrasse 5, 64283, Darmstadt, Germany

    Reiner Wichert

  7. Informatics Institute, University of Amsterdam, Science Park 904, 1098XH, Amsterdam, The Netherlands

    Gwenn Englebienne

  8. Ambient Intelligence Research Lab., Stanford University and Ghent University, 350 Serra Mall, David Packard #318, 94305-9515, Stanford, CA, USA

    Hamid Aghajan

  9. Informatics Institute, University of Amsterdam and Amsterdam University of Applied Sciences, Science Park 904, 1098XH, Amsterdam, The Netherlands

    Ben J. A. Kröse

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Filippaki, C., Antoniou, G., Tsamardinos, I. (2011). Using Constraint Optimization for Conflict Resolution and Detail Control in Activity Recognition. In: Keyson, D.V., et al. Ambient Intelligence. AmI 2011. Lecture Notes in Computer Science, vol 7040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25167-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-25167-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25166-5

  • Online ISBN: 978-3-642-25167-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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