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A Study of Ex Ante Law Enforcement in Norm-Governed Learning Agents

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New Frontiers in Artificial Intelligence (JSAI-isAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7856))

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Abstract

We investigate ex ante law enforcement within a population of norm-governed learning agents using a probabilistic rule-based argumentation framework. We show that this formal framework can advantageously complete a traditional analysis based on expected utilities, in particular when hyper-rational or omniscient agents are not assumed. This has significant implications for the design of self-organising electronic institutions, where the cost of monitoring and enforcement of laws and norms has to be taken into consideration.

Part of this work has been carried out in the scope of the EC co-funded project SMART (FP7-287583). This work is partially supported by the EU Marie Curie Intra-European Fellowship 274057.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Eng., Imperial College London, UK

    Régis Riveret, Dídac Busquets & Jeremy Pitt

  2. CIRSFID and Department of Legal Studies, University of Bologna, Bologna, Italy

    Giuseppe Contissa, Antonino Rotolo & Giovanni Sartor

  3. European University Institute, Florence, Italy

    Giuseppe Contissa & Giovanni Sartor

Authors
  1. Régis Riveret
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  2. Dídac Busquets
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  3. Jeremy Pitt
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  4. Giuseppe Contissa
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  5. Antonino Rotolo
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  6. Giovanni Sartor
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Editor information

Editors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology (AIST), Japan

    Yoichi Motomura

  2. Tohoku University, Kawauchi 41, 980-8576, Aoba-ku, Sendai, Japan

    Alastair Butler

  3. Ochanomizu University, 2-1-1 Bunkyo-ku, 122-8610, Tokyo, Japan

    Daisuke Bekki

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Riveret, R., Busquets, D., Pitt, J., Contissa, G., Rotolo, A., Sartor, G. (2013). A Study of Ex Ante Law Enforcement in Norm-Governed Learning Agents. In: Motomura, Y., Butler, A., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2012. Lecture Notes in Computer Science(), vol 7856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39931-2_12

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  • DOI: https://doi.org/10.1007/978-3-642-39931-2_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39930-5

  • Online ISBN: 978-3-642-39931-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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