A self-adaptive neural learning classifier system with constructivism for mobile robot control

Abstract

For artificial entities to achieve true autonomy and display complex life-like behaviour they will need to exploit appropriate adaptable learning algorithms. In this sense adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviours. This paper examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture which exploits parameter selfadaptation as an approach to realise such behaviour. The system uses a rule structure in which each is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented from using a mobile robot platform. © Springer-Verlag 2004.