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International Conference on Autonomous Agents and Multiagent Systems

AAMAS 2017: Autonomous Agents and Multiagent Systems pp 194–207Cite as

Norm Conflict Identification Using Deep Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10643))

Abstract

Contracts represent agreements between two or more parties formally in the form of deontic statements or norms within their clauses. If not carefully designed, such conflicts may invalidate an entire contract, and thus human reviewers invest great effort to write conflict-free contracts that, for complex and long contracts, can be time consuming and error-prone. In this work, we develop an approach to automate the identification of potential conflicts between norms in contracts. We build a two-phase approach that uses traditional machine learning together with deep learning to extract and compare norms in order to identify conflicts between them. Using a manually annotated set of conflicts as train and test set, our approach obtains 85% accuracy, establishing a new state-of-the art.

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Notes

  1. 1.

    https://goo.gl/3Hbl1r.

  2. 2.

    http://contracts.onecle.com/.

  3. 3.

    http://contracts.onecle.com/type/47.shtml.

  4. 4.

    http://sourceforge.net/projects/jirs/.

  5. 5.

    http://contracts.onecle.com.

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Acknowledgements

We gratefully thank Google Research Awards for Latin America for funding our project.

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Authors and Affiliations

  1. School of Computer Science, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil

    João Paulo Aires & Felipe Meneguzzi

Authors
  1. João Paulo Aires
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  2. Felipe Meneguzzi
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Corresponding author

Correspondence to João Paulo Aires .

Editor information

Editors and Affiliations

  1. University of Central Florida, Orlando, Florida, USA

    Gita Sukthankar

  2. IIIA-CSIC, Bellaterra, Spain

    Juan A. Rodriguez-Aguilar

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Aires, J.P., Meneguzzi, F. (2017). Norm Conflict Identification Using Deep Learning. In: Sukthankar, G., Rodriguez-Aguilar, J. (eds) Autonomous Agents and Multiagent Systems. AAMAS 2017. Lecture Notes in Computer Science(), vol 10643. Springer, Cham. https://doi.org/10.1007/978-3-319-71679-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-71679-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71678-7

  • Online ISBN: 978-3-319-71679-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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