We have witnessed considerable research investigating trust between agents in multi-agent systems. However, the issue of trust between agents and users has rarely been reported in the literature. In this paper, we describe our experiences with ITRUST, a multi-agent artificial market system whose software broker agent can learn to build a relatively long-term trust relationship with their clients. The goals of these broker agents are not only to maximize the total revenue subject to their clients' risk preference as most other agents do in [10, 14, 17], but also to maximize the trust they receive from their clients. Trust is introduced into I-TRUST as a relationship between clients and their software broker agents in terms of the amount of money they are willing to give to these agents to invest on their behalf. To achieve this, broker agents first elicit user models explicitly through questionnaires and implicitly through three games. Then based on the initial user models, they will learn to invest and later update the models when necessary
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