Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Trust beten agents has been explored extensively in the literature. Hover, trust beten agents and users has largely been left untouched. ,  report our preliminary results of how reinforcement-learning agents (i.e. broker agents, or brokers) win the trust of their client in an artificial market I-TRUST. 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 [LeBaron et al. 1997; Parkes and Huberman 2001; Schroeder et al. 2000], but also to maximize the trust they receive from their clients. Trust is introduced into I-TRUST as a relationship beten 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 must first elicit user models both explicitly through questionnaires and implicitly through three games. Then based on the initial user models, a broker agent will learn to invest and later update the model when necessary. In addition to the broker agent’s individual learning of how to maximize the ‘reward’ he may receive from his client,  have incorporated agents’ cooperative reinforcement learning to adjust their portfolio selecting strategy, which is implemented in FIPA-OS. A large-scale experiment is expected as our future research.
['Tiffany Ya Tang', 'Pinata Winoto', 'Xiaolin Niu']
I am writing a research paper on a idea I-TRUST: investigating trust between users and agents in a multi-agent portfolio management system
[{"from": "human", "value": "I am writing a research paper on a idea I-TRUST: investigating trust between users and agents in a multi-agent portfolio management system"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Trust beten agents has been explored extensively in the literature. Hover, trust beten agents and users has largely been left untouched. ,  report our preliminary results of how reinforcement-learning agents (i.e. broker agents, or brokers) win the trust of their client in an artificial market I-TRUST. The goals of these broker agents are not only to maximize the total revenue subject to their clients\u2019 risk preference as most other agents do in [LeBaron et al. 1997; Parkes and Huberman 2001; Schroeder et al. 2000], but also to maximize the trust they receive from their clients. Trust is introduced into I-TRUST as a relationship beten 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 must first elicit user models both explicitly through questionnaires and implicitly through three games. Then based on the initial user models, a broker agent will learn to invest and later update the model when necessary. In addition to the broker agent\u2019s individual learning of how to maximize the \u2018reward\u2019 he may receive from his client,  have incorporated agents\u2019 cooperative reinforcement learning to adjust their portfolio selecting strategy, which is implemented in FIPA-OS. A large-scale experiment is expected as our future research."}]