By Topic

A layered approach to learning intelligent behaviours in rescue robot simulation system using fuzzy logic and neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)

RoboCup Rescue Simulation System is a particularly challenging domain for studying multi agent system and multi agent learning. Machine learning has become a key solution to complicated multi agent tasks. In this paper, using machine learning as a tool for arriving at intelligent and efficient behaviors for Rescue robots involves layering increasingly complex learning behaviors. We describe multiple levels of learned behaviors. First the robots try to lean basic knowledge about their environment's characteristics like the spreading speed of tire in the city after earthquake, or their ability to extinguish fires in different situations. ANN has been used to achieve these goals. Afterwards, using these learned components, they learn low level skills for lire extinguishment. Finally, in the next level they exploit fuzzy logic for planning their high level strategy toward their goal.

Published in:

Automation Congress, 2004. Proceedings. World  (Volume:17 )

Date of Conference:

June 28 2004-July 1 2004