A new method for learning and tuning a fuzzy logic controller based on reinforcements from
a dynamic system is presented. In particular, our Generalized Approximate Reasoning …

Fuzzy Actor-Critic Learning (FACL) and Fuzzy Q-Learning (FQL) are reinforcement learning
methods based on dynamic programming (DP) principles. In the paper, they are used to …

The development of techniques for autonomous navigation in real-world environments
constitutes one of the major trends in the current research on robotics. An important problem …

Fuzzy logic methods have been used successfully in many real-world applications, but the
foundations of fuzzy logic remain under attack. Taken together, these two facts constitute a …

Fuzzy Set Theory, introduced by Zadeh in 1965 [77], has been the subject of much
controversy and debate. In recent years, it has found many applications in a variety of fields …

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller.
A reinforcement learning technique is used in conjuction with a multilayer neural network …

Most current architectures for autonomous robots are based on a decomposition of the
control problem into small units of control, or behaviors. While this decomposition has a …

This paper addresses the field of humanoid and personal robotics—its objectives,
motivations, and technical problems. The approach described in the paper is based on the …

Fuzzy logic has virtually exploded over the landscape of emerging technologies, becoming
an integral part of myriad applications and a standard tool for engineers. Until recently, most …

The authors present an approach to generating and tuning a knowledge base for fuzzy logic
control (FLC) of an inverted pendulum. They used a modified self-organizing control …