A value-based trust assessment model for multi-agent systems

Kinzang Chhogyal, Abhaya Nayak, Aditya Ghose, Hoa K. Dam

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

An agent's assessment of its trust in another agent is commonly taken to be a measure of the reliability/predictability of the latter's actions. It is based on the trustor's past observations of the behaviour of the trustee and requires no knowledge of the inner-workings of the trustee. However, in situations that are new or unfamiliar, past observations are of little help in assessing trust. In such cases, knowledge about the trustee can help. A particular type of knowledge is that of values - things that are important to the trustor and the trustee. In this paper, based on the premise that the more values two agents share, the more they should trust one another, we propose a simple approach to trust assessment between agents based on values, taking into account if agents trust cautiously or boldly, and if they depend on others in carrying out a task.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-2019)
EditorsSarit Kraus
Place of PublicationCalifornia
PublisherInternational Joint Conferences on Artificial Intelligence
Pages194-200
Number of pages7
ISBN (Electronic)9780999241141
DOIs
Publication statusPublished - Aug 2019
Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
Duration: 10 Aug 201916 Aug 2019

Conference

Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
CountryChina
CityMacao
Period10/08/1916/08/19

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  • Cite this

    Chhogyal, K., Nayak, A., Ghose, A., & Dam, H. K. (2019). A value-based trust assessment model for multi-agent systems. In S. Kraus (Ed.), Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-2019) (pp. 194-200). California: International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/28