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A Fuzzy Logic Based Policy Negotiation Model

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Knowledge Science, Engineering and Management (KSEM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10412))

Abstract

Few existing policy generation models can reflect the fact that self-interested stockholders often need to interact when generating a set of policies acceptable to them all. To this end, this paper proposes a negotiation model for policy generation. In this model, each negotiating agent employs a fuzzy logic system to evaluate each policy in a proposal made by others during their negotiation, and then uses a uninorm operator to aggregate the evaluations of all the single policies in the proposal to gain an overall evaluation of the proposal. Moreover, different negotiating agent can use different fuzzy reasoning systems. Finally, we do some experiments to reveal some insights into our model.

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Acknowledgements

This research was partially supported by the Natural Science Foundation of Guangdong Province, China (Nos. 2016A030313231 and S2012030006242), the National Natural Science Foundation of China (No. 61272066), the key project in universities in Guangdong Province of China (No. 2016KZDXM024), the Innovation project of postgraduate education in Guangdong Province of China (No. 2016SFKC_13), and the Project of Science and Technology in Guangzhou in China (Nos. 2014J4100031 and 201604010098).

Author information

Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, China

    Jieyu Zhan, Yuncheng Jiang & Wenjun Ma

  2. Department of Information and Management Science, Guangxi Normal University, Guilin, China

    Xudong Luo

  3. School of Management, Xiamen University, Xiamen, China

    Mukun Cao

Authors
  1. Jieyu Zhan
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  2. Xudong Luo
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  3. Yuncheng Jiang
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  4. Wenjun Ma
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  5. Mukun Cao
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Corresponding authors

Correspondence to Xudong Luo or Yuncheng Jiang .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, Victoria, Australia

    Gang Li

  2. University of Arizona, Tucson, Arizona, USA

    Yong Ge

  3. Southwest University, Chongqing, China

    Zili Zhang

  4. Peking University, Beijing, China

    Zhi Jin

  5. University of Technology Sydney, Sydney, New South Wales, Australia

    Michael Blumenstein

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Zhan, J., Luo, X., Jiang, Y., Ma, W., Cao, M. (2017). A Fuzzy Logic Based Policy Negotiation Model. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-63558-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63557-6

  • Online ISBN: 978-3-319-63558-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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