Strathprints Home | Open Access | Browse | Search | User area | Copyright | Help | Library Home | SUPrimo

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. [Proceedings Paper]

Full text not available in this repository. (Request a copy from the Strathclyde author)

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.

Item type: Proceedings Paper
ID code: 32308
Keywords: back-propagation, genetic algorithms, machine learning, multi-agent, simulated worlds, Electronic computers. Computer science
Subjects: Science > Mathematics > Electronic computers. Computer science
Department: Faculty of Science > Computer and Information Sciences
Related URLs:
Depositing user: Pure Administrator
Date Deposited: 26 Jul 2011 10:23
Last modified: 17 Jul 2013 13:48
URI: http://strathprints.strath.ac.uk/id/eprint/32308

Actions (login required)

View Item