Abstract
A key issue to realize community of agents involving social aspects is that of modeling trustworthiness between the actors of the society and, to this purpose, many trust and reputation models have been proposed in the past. Most of these proposals focused on representing trust dimensions using apposite scalar measures, and integrating these measures in synthetic indicators of trustworthiness, possibly collected into a trust vector. We highlight as this choice is an evident limitation and to overcome it, we propose a new model of trust and reputation for a community of social agents, where the trust perceived by an agent about another agent is modeled by a directed, weighted graph whose nodes and edges represent trust dimensions and their relationships, respectively. This way, we can represent also those situations in which an agent does not knows a given trust dimension, e.g. the honesty, but it is capable to derive it from other correlated dimensions, e.g. the reliability. Our model, called T-pattern, has been specifically designed to represent any situation in which many different trust dimensions are mutually dependent. We also introduce the notion of T-Pattern Network (TPN) as an integrated framework to represent both the trust and reputation values as well as the dependencies between trust dimensions for all the pairs of agents.
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Rosaci, D., Sarné, G.M.L. (2022). T-Patterns: A Way to Model Complex Trustworthiness in a Social Multi-agent Community. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_27
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DOI: https://doi.org/10.1007/978-3-030-96627-0_27
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