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HARM: A Hybrid Rule-Based Agent Reputation Model Based on Temporal Defeasible Logic

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Rules on the Web: Research and Applications (RuleML 2012)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7438))

Abstract

Multi-agent systems are considered a modern medium of communication and interaction with limited or no human intervention. As intelligent agents are gradually enriched with Semantic Web technology, their use is constantly increasing. To this end, the degree of trust that can be invested in a certain agent is recognized as a vital issue. Current trust models are mainly based on agents’ direct experience (interaction trust) or reports provided by others (witness reputation). Though, lately, some combinations of them (hybrid models) were also proposed. To overcome their main drawbacks, in this paper we propose HARM, a hybrid, rule-based reputation model based on temporal defeasible logic. It combines the advantages of the hybrid approach and the benefits of a rule-based reputation modeling approach, providing a stable and realistic estimation mechanism with low bandwidth and computational complexity. Moreover, an evaluation of the reputation model is presented, demonstrating the added value of the approach.

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Author information

Authors and Affiliations

  1. Dept. of Informatics, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, Greece

    Kalliopi Kravari & Nick Bassiliades

Authors
  1. Kalliopi Kravari
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  2. Nick Bassiliades
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Editor information

Editors and Affiliations

  1. Department of Information Studies, University College London, Gower Street, WC1E 6BT, London, UK

    Antonis Bikakis

  2. Department of Databases and Information Systems, Brandenburg University of Technology at Cottbus, Walther Pauer Str. 2, 03046, Cottbus, Germany

    Adrian Giurca

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Kravari, K., Bassiliades, N. (2012). HARM: A Hybrid Rule-Based Agent Reputation Model Based on Temporal Defeasible Logic. In: Bikakis, A., Giurca, A. (eds) Rules on the Web: Research and Applications. RuleML 2012. Lecture Notes in Computer Science, vol 7438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32689-9_15

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  • DOI: https://doi.org/10.1007/978-3-642-32689-9_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32688-2

  • Online ISBN: 978-3-642-32689-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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