Please use this identifier to cite or link to this item:
Type: Artigo de evento
Title: Analysing Learning Classifier Systems In Reactive And Non-reactive Robotic Tasks
Author: Moioli R.C.
Vargas P.A.
Von Zuben F.J.
Abstract: There are few contributions to robot autonomous navigation applying Learning Classifier Systems (LCS) to date. The primary objective of this work is to analyse the performance of the strength-based LCS and the accuracy-based LCS, named EXtended Learning Classifier System (XCS), when applied to two distinct robotic tasks. The first task is purely reactive, which means that the action to be performed can rely only on the current status of the sensors. The second one is non-reactive, which means that the robot might use some kind of memory to be able to deal with aliasing states. This work presents a rule evolution analysis, giving examples of evolved populations and their peculiarities for both systems. A review of LCS derivatives in robotics is provided together with a discussion of the main findings and an outline of future investigations. © 2008 Springer Berlin Heidelberg.
Rights: fechado
Identifier DOI: 10.1007/978-3-540-88138-4-17
Date Issue: 2008
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File SizeFormat 
2-s2.0-57049085207.pdf800.51 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.