Neural fuzzy agents that learn profiles and search databases

This paper shows how 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