Performance, robustness and effort cost comparison of machine learning mechanisms in FlatLand

Yannakakis, G. N. and Levine, J. and J. Hallam, J. and Papageorgiou, M.; (2003) Performance, robustness and effort cost comparison of machine learning mechanisms in FlatLand. In: 11th IEEE Mediterranean Conference on Control and Automation (MED'03). UNSPECIFIED.

Full text not available in this repository.Request a copy

Abstract

This paper presents the first stage of research into a multi-agent complex environment, called “FlatLand” aiming at emerging complex and adaptive obstacle-avoidance and targetachievement behaviors by use of a variety of learning mechanisms. The presentation includes a detailed description of the FlatLand simulated world, the learning mechanisms used as well as an efficient method for comparing the mechanisms’ performance, robustness and required computational effort.