Neural fuzzy agents that learn a user's preference map
Sanya Mitaim, Bart Kosko · 2002
This paper models an intelligent agent that helps a user search or filter images in the database. Database search depends on the user's profile of likes and dislikes and how the user ranks similar images. A neural fuzzy system can help learn an agent profile of a user. The fuzzy system uses if-then rules that store and compress the agent's knowledge of the user's likes and dislikes. A neural system uses training data to form and tune the rules. The profile is a preference map or a bumpy utility surface over the space of search objects. Rules define fuzzy patches that cover the bumps as learning unfolds and as the fuzzy agent system gives a finer approximation of the profile. The agent system searches for preferred objects with the learned profile and a new fuzzy measure of similarity. We derive a new supervised learning law that tunes this matching measure with new sample data. Then we test the fuzzy agent profile system on object spaces of flowers and sunsets and test the fuzzy agent matching system on an object space of sunset images.