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Agent-Based Assistance in Ambient Assisted Living Through Reinforcement Learning and Semantic Technologies

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On the Move to Meaningful Internet Systems. OTM 2017 Conferences (OTM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10574))

Abstract

For impaired people, the conduction of certain daily life activities is problematic due to motoric and cognitive handicaps. For that reason, assistive agents in ambient assisted environments provide services that aim at supporting elderly and impaired people. However, these agents act in complex stochastic and indeterministic environments where the concrete effects of a performed action are usually unknown at design time. Furthermore, they have to perform varying tasks according to the user’s context and needs, wherefore an agent has to be flexible and able to recognize required capabilities in a certain situation in order to provide adequate, unobtrusive assistance. Hence, an expressive representation framework is required that relates user-specific impairments to required agent capabilities. This work presents an approach which (a) describes and links user impairments and capabilities using the formal, model-theoretic semantics expressed in OWL2 DL ontologies, (b) computes optimal policies through Reinforcement Learning and propagates these in an agent network. The presented approach improves the collaborative, personalized and adequate assistance of assistive agents and tailors the agent-based services to the user’s missing capabilities.

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Notes

  1. 1.

    A policy is a strategy for performing the best possible action in a certain state.

  2. 2.

    A content management platform for generating a light-weight RDF(S) representation of annotated wiki pages. See: https://www.semantic-mediawiki.org/wiki/Semantic_MediaWiki.

  3. 3.

    https://www.docker.com/.

  4. 4.

    Internet of Things.

  5. 5.

    Further details: https://www.w3.org/TR/2012/REC-owl2-profiles-20121211/.

  6. 6.

    https://www.w3.org/Submission/SWRL/.

  7. 7.

    https://www.mediawiki.org/wiki/API:Main_page.

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Author information

Authors and Affiliations

  1. FZI Forschungszentrum Informatik am KIT, Information Process Engineering, Haid-und-Neu-Str. 10-14, 76131, Karlsruhe, Germany

    Nicole Merkle

  2. Institute for Computer Science, University of Applied Sciences Darmstadt, Schöfferstrasse 8B, 64295, Darmstadt, Germany

    Stefan Zander

Authors
  1. Nicole Merkle
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  2. Stefan Zander
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Corresponding author

Correspondence to Nicole Merkle .

Editor information

Editors and Affiliations

  1. University of Lorraine, Nancy, France

    Hervé Panetto

  2. Odisee University College, Brussels, Belgium

    Christophe Debruyne

  3. Télécom SudParis, Évry, France

    Walid Gaaloul

  4. Tilburg University, Tilburg, The Netherlands

    Mike Papazoglou

  5. Freie Universität Berlin and Fraunhofer FOKUS, Berlin, Germany

    Adrian Paschke

  6. Università degli Studi di Milano, Crema, Italy

    Claudio Agostino Ardagna

  7. TU Graz, Graz, Austria

    Robert Meersman

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Merkle, N., Zander, S. (2017). Agent-Based Assistance in Ambient Assisted Living Through Reinforcement Learning and Semantic Technologies. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10574. Springer, Cham. https://doi.org/10.1007/978-3-319-69459-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-69459-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69458-0

  • Online ISBN: 978-3-319-69459-7

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