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This paper proposes a fuzzy agent-based approach to assist the product configuration.
This paper proposes a new type of personalized recommendation agents called fuzzy cognitive agents.
The case study demonstrates that the fuzzy cognitive agent is both flexible and effective in supporting e-commerce applications.
A case study is included to illustrate how personalized recommendations are made by fuzzy cognitive agents in e-commerce sites.
We present the structure of a fuzzy controller-agent (FCA) and propose the tuning of parameters of the FLC by genetic algorithms (GAs).
Fuzzy cognitive agents are designed to give personalized suggestions based on the user's current personal preferences, other user's common preferences, and expert's domain knowledge.
Fuzzy cognitive agents are able to represent knowledge via extended fuzzy cognitive maps, to learn users' preferences from most recent cases and to help customers make inferences and decisions through numeric computation instead of symbolic and logic deduction.
The fuzzy consensual solution agents emerge from fuzzy interactions of fuzzy distributed agents.
The optimal product configuration emerges from affinities of the fuzzy consensual solution agents.
In doing so we present a novel approach to the implementation of IB agents based on a hierarchical fuzzy genetic multi-embedded-agent architecture comprising a low-level behaviour based reactive layer whose outputs are co-ordinated in a fuzzy way according to deliberative plans.
The fuzzy rules of each agent are written considering the state of other agents besides its own state.
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