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

G. N. Yannakakis, J. Levine, J. J. Hallam, M. Papageorgiou

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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.
Original languageEnglish
Title of host publication11th IEEE Mediterranean Conference on Control and Automation (MED'03)
Number of pages6
Publication statusPublished - 1 Jun 2003

Keywords

  • back-propagation
  • genetic algorithms
  • machine learning
  • multi-agent
  • simulated worlds

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