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Self-Adaptive Organizations for Distributed Search: The Case of Reinforcement Learning

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Distributed Computing and Artificial Intelligence, 13th International Conference

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 474))

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Abstract

In this paper we study the effects of learning by reinforcement and adaptive change of distributed search systems’ organizations. We find that employing learning by reinforcement to direct organizational alterations of distributed search systems may lead to high levels of systems’ performance and this, in particular, with rather high efficiency in terms of effort of reorganization. The results also suggest that the complexity of the search problem together with the aspiration level, relevant for the positive or negative reinforcement, considerably shape the effects of learning.

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Authors and Affiliations

  1. Alpen-Adria-Universitaet Klagenfurt, 9020, Klagenfurt, Austria

    Friederike Wall

Authors
  1. Friederike Wall
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Correspondence to Friederike Wall .

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Editors and Affiliations

  1. Faculty of Engineering, Osaka Institute of Technology Faculty of Engineering, Osaka, Osaka, Japan

    Sigeru Omatu

  2. Faculty of Computer Science & Informatio, Universiti Teknologi Malaysia (UTM) Faculty of Computer Science & Informatio, Baharu, Malaysia

    Ali Semalat

  3. Department of Electronics and Compu, Koszalin University of Technology Department of Electronics and Compu, Koszalin, Poland

    Grzegorz Bocewicz

  4. Faculty of Electrical Engineering and Cs, Kielce University of Technology Faculty of Electrical Engineering and Cs, Kielce, Poland

    Paweł Sitek

  5. Faculty of Engineering and Science, Aalborg University Faculty of Engineering and Science, Aalborg, Denmark

    Izabela E. Nielsen

  6. ETS Ingeniería Informática, University of Sevilla ETS Ingeniería Informática, Sevilla, Spain

    Julián A. García García

  7. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid Departamento de Inteligencia Artificial, Madrid, Spain

    Javier Bajo

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Wall, F. (2016). Self-Adaptive Organizations for Distributed Search: The Case of Reinforcement Learning. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-40162-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-40162-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40161-4

  • Online ISBN: 978-3-319-40162-1

  • eBook Packages: EngineeringEngineering (R0)

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