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Fuzzy number approach to trust in coalition environment

Published: 25 July 2005 Publication History

Abstract

General trust management model that we present is adapted for ad-hoc coalition environment, rather than for classic client-supplier relationship. The trust representation used in the model extends the current work by using the fuzzy number approach, readily representing the trust uncertainty without sacrificing the simplicity. The model contains the trust representation part, decision-making part and a learning part. In our representation, we define the trusted agents as a type-2 fuzzy set. In a decision-making part, we use the methods from the fuzzy rule computation and fuzzy control to take trusting decision. For trust learning, we use a strictly iterative approach. We verify our model in a multi-agent simulation where the agents in the community learn to identify and refuse the defectors. Our simulation contains the environment-caused involuntary failure used as a background noise that makes the trust-learning difficult.

References

[1]
D. Dubois and H. Prade. Fuzzy real algebra:some results. Fuzzy Sets and Systems, 2(4):327--348, 1979.
[2]
S. Ramchurn, D. Huynh, and N. R. Jennings. Trust in multiagent systems. The Knowledge Engineering Review, 19(1), 2004.

Cited By

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  • (2018)Statistical relational learning of trustMachine Language10.1007/s10994-010-5211-x82:2(191-209)Online publication date: 30-Dec-2018
  • (2012)Framework for assessing the trustworthiness of cloud resources2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support10.1109/CogSIMA.2012.6188367(142-145)Online publication date: Mar-2012

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    cover image ACM Conferences
    AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
    July 2005
    1407 pages
    ISBN:1595930930
    DOI:10.1145/1082473
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 25 July 2005

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    Author Tags

    1. coalitions
    2. fuzzy numbers
    3. reputation
    4. trust

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    Cited By

    View all
    • (2018)Statistical relational learning of trustMachine Language10.1007/s10994-010-5211-x82:2(191-209)Online publication date: 30-Dec-2018
    • (2012)Framework for assessing the trustworthiness of cloud resources2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support10.1109/CogSIMA.2012.6188367(142-145)Online publication date: Mar-2012

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