A Dynamic Framework of Reputation Systems for an Agent Mediated e-market
The success of an agent mediated e-market system lies in the
underlying reputation management system to improve the quality
of services in an information asymmetric e-market. Reputation
provides an operatable metric for establishing trustworthiness
between mutually unknown online entities. Reputation systems
encourage honest behaviour and discourage malicious behaviour
of participating agents in the e-market. A dynamic reputation
model would provide virtually instantaneous knowledge about
the changing e-market environment and would utilise Internets’
capacity for continuous interactivity for reputation computation.
This paper proposes a dynamic reputation framework using
reinforcement learning and fuzzy set theory that ensures
judicious use of information sharing for inter-agent cooperation.
This framework is sensitive to the changing parameters of emarket
like the value of transaction and the varying experience of
agents with the purpose of improving inbuilt defense mechanism
of the reputation system against various attacks so that e-market
reaches an equilibrium state and dishonest agents are weeded out
of the market.
Keywords: Reputation, Reinforcement Learning, Fuzzy attribute weights, e-market
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