X-TCS: Accuracy-based learning classifier system robotics

Studley, M. and Bull, L. (2005) X-TCS: Accuracy-based learning classifier system robotics. 2005 IEEE Congress on Evolutionary Computation, 3. pp. 2099-2106.

Full text not available from this repository

Publisher's URL: http://dx.doi.org/10.1109/CEC.2005.1554954

Abstract

Although most learning classifier system (LCS) research uses the accuracy-based XCS, it had never been used to control a physical robot before. In comparison to purely evolutionary or purely reinforcement learning approaches, an LCS should be faster to learn than the former, and require less operator input and additional techniques than the latter. Increased learning speed removes the need to build simulations, and all learning can take place in situ on the robot. Learning is online and continuous, not 'learn, then perform'. This paper presents some simple extensions to XCS which enable it to learn to optimally control a robot in a simple task. There is little need for tuning of the algorithm, less need to make decisions a priori to guide the solution of the problem, and the robot learns optimal behaviour in hours rather than the week previously reported with evolutionary techniques in a similar problem.

Item Type:Article
Uncontrolled Keywords:robotics, learning classifier systems
Faculty/Department:Faculty of Environment and Technology
ID Code:5916
Deposited By: R. Upload account
Deposited On:22 Jan 2010 15:10
Last Modified:22 May 2014 14:11

Request a change to this item

Copyright 2013 © UWE better together