Neurofuzzy agents and neurofuzzy laws for autonomous machine learning and control

Abstract

Real world autonomous agents exhibit adaptive, incremental, exploratory, and sometimes explosive learning behaviors. Learning in neurofuzzy control, however, is often referred to as global training with a large set of random examples and with a very low learning rate. This type of controller does not show exploratory learning behaviors. An agent-oriented approach to neurofuzzy control is introduced and illustrated in folding-legged robot locomotion and gymnastics: necessary and sufficient conditions are established for agent-oriented neurofuzzy discovery; and a theory of coordinated multiagent neurofuzzy control is analytically formulated. The analytical features bridge a gap between linear control, neurofuzzy control, adaptive learning, and exploratory learning.