In this paper, we provide an overview of the existing methods for integrating human advice
into a Reinforcement Learning process. We first propose a taxonomy of the different forms of …

Learning, planning, and representing knowledge at multiple levels of temporal abstraction
are key, longstanding challenges for AI. In this paper we consider how these challenges can …

Evolutionary Robotics is a method for automatically generating artificial brains and
morphologies of autonomous robots. This approach is useful both for investigating the …

Broadly conceived as computational models of cognition and tools for modeling complex
adaptive systems, later extended for use in adaptive robotics, and today also applied to …

In this paper we describe the evolution of a discrete-time recurrent neural network to control
a real mobile robot. In all our experiments the evolutionary procedure is carried out entirely …

Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to
maintain the correctness, consistency, and completeness of a fuzzy rule base constructed …

Providing a guided tour of the pioneering work and major technical issues, Multiagent
Robotic Systems addresses learning and adaptation in decentralized autonomous robots. Its …

This paper deals with a fuzzy-based intelligent robotic system that requires various
capabilities normally associated with intelligence. It acquires skills and knowledge through …

We present the results of a research aimed at improving the Q-learning method through the
use of artificial neural networks. Neural implementations are interesting due to their …

Instrumental (or operant) conditioning, a form of animal learning, is similar to reinforcement
learning (Watkins, 1989) in that it allows an agent to adapt its actions to gain maximally from …