Robot Learning Using Learning Classifier Systems Approach

Abstract

Efforts to develop highly complex and adaptable machines that meet the ideal of mechanical human equivalents are now reaching the proof-of concept stage. Enabling a human to efficiently transfer knowledge and skills to a machine has inspired decades of research. I present a learning mechanism in which a robot learns new tasks using genetic-based machine learning technique, learning classifier system (LCS). LCSs are rule-based systems that automatically build their ruleset. At the origin of Holland’s work, LCSs were seen as a model of the emergence of cognitive abilities thanks to adaptive mechanisms, particularly evolutionary processes. After a renewal of the field more focused on learning, LCSs are now considered as sequential decision problem-solving systems endowed with a generalization property. Indeed, from a Reinforcement Learning point of view, LCSs can be seen as learning systems building a compact representation of their problem. More recently, LCSs have proved efficient at solving automatic classification tasks (Sigaud et al., 2007). The aim of the present contribution is to describe the state-of the-art of LCSs, emphasizing recent developments, and focusing more on the application of LCS for Robotics domain. In previous robot learning studies, optimization of parameters has been applied to acquire suitable behaviors in a real environment. Also in most of such studies, a model of human evaluation has been used for validation of learned behaviors. However, since it is very difficult to build human evaluation function and adjust parameters, a system hardly learns behavior intended by a human operator. In order to reach that goal, I first present the two mechanisms on which they rely, namely GAs and Reinforcement Learning (RL). Then I provide a brief history of LCS research intended to highlight the emergence of three families of systems: strength-based LCSs, accuracy-based LCSs, and anticipatory LCSs (ALCSs) but mainly XCS as XCS, is the most studied LCS at this time. Afterward, in section 5, I present some examples of existing LCSs which have LCS applied for robotics. The next sections are dedicated to the particular aspects of theoretical and applied extensions of Intelligent Robotics. Finally, I try to highlight what seem to be the most promising lines of research given the current state of the art, and I conclude with the available resources that can be consulted in order to get a more detailed knowledge of these systems.