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Using Automated Approximate Satisfaction in Parameter Search for Dynamic Agent Models

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Multi-Agent Systems and Agreement Technologies (EUMAS 2016, AT 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10207))

Abstract

Numerical agent models often include a number of parameters. The values of such parameters are usually determined by using some numerical parameter tuning method based on numerical empirical data. However, in many cases no numerical empirical data are available, but properties for dynamic patterns are known that should be fulfilled, as requirements. Classical numerical parameter tuning methods normally cannot work with such dynamic properties, as they can only be true or false. To remedy this, in this paper the notion of approximate satisfaction of dynamic properties is introduced. It adds a numerical measure to the logical notion of satisfaction. By doing this, numerical optimization methods for parameter estimation become applicable to support the design of dynamic agent models for which dynamic properties have been specified as requirements.

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Author information

Authors and Affiliations

  1. Behavioural Informatics Group, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

Authors
  1. Jan Treur
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Correspondence to Jan Treur .

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Editors and Affiliations

  1. King’s College London, London, United Kingdom

    Natalia Criado Pacheco

  2. Polytechnic University of Valencia, Valencia, Portugal

    Carlos Carrascosa

  3. Artificial Intelligence Research Institute (IIIA-CSIC), Barcelona, Spain

    Nardine Osman

  4. Polytechnic University of Valencia, Valencia, Spain

    Vicente Julián Inglada

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Treur, J. (2017). Using Automated Approximate Satisfaction in Parameter Search for Dynamic Agent Models. In: Criado Pacheco, N., Carrascosa, C., Osman, N., Julián Inglada, V. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2016 2016. Lecture Notes in Computer Science(), vol 10207. Springer, Cham. https://doi.org/10.1007/978-3-319-59294-7_19

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  • DOI: https://doi.org/10.1007/978-3-319-59294-7_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59293-0

  • Online ISBN: 978-3-319-59294-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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