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

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

doi:10.24963/ijcai.2019/28

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

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

Department of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-2019)
Editors	Sarit Kraus
Place of Publication	California
Publisher	International Joint Conferences on Artificial Intelligence
Pages	194-200
Number of pages	7
ISBN (Electronic)	9780999241141
DOIs	https://doi.org/10.24963/ijcai.2019/28
Publication status	Published - Aug 2019
Event	28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China Duration: 10 Aug 2019 → 16 Aug 2019

Conference

Conference	28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Country	China
City	Macao
Period	10/08/19 → 16/08/19

Access to Document

10.24963/ijcai.2019/28

Link to publication in Scopus

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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

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title = "A value-based trust assessment model for multi-agent systems",

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.",

author = "Kinzang Chhogyal and Abhaya Nayak and Aditya Ghose and Dam, {Hoa K.}",

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Chhogyal, K, Nayak, A, Ghose, A & Dam, HK 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). International Joint Conferences on Artificial Intelligence, California, pp. 194-200, 28th International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10/08/19. https://doi.org/10.24963/ijcai.2019/28

A value-based trust assessment model for multi-agent systems. / Chhogyal, Kinzang; Nayak, Abhaya; Ghose, Aditya; Dam, Hoa K.

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-2019). ed. / Sarit Kraus. California : International Joint Conferences on Artificial Intelligence, 2019. p. 194-200.

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

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