Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours

The four basic behaviours of mobile robot are chasing, approaching, avoiding and escaping. The main problem in robotic system is in selecting the correct behaviour. The aim of this research is to overcome the behaviour selection problem. This thesis proposes methods that can overcome the problems of...

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Main Author:	Baneamoon, Saeed Mohammed Saeed
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/41974/1/SAEED_MOHAMMED_SAEED_BANEAMOON_HJ.pdf
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id	my-usm-ep.41974
record_format	uketd_dc
spelling	my-usm-ep.419742019-04-12T05:26:46Z Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours 2010-11 Baneamoon, Saeed Mohammed Saeed QA75.5-76.95 Electronic computers. Computer science The four basic behaviours of mobile robot are chasing, approaching, avoiding and escaping. The main problem in robotic system is in selecting the correct behaviour. The aim of this research is to overcome the behaviour selection problem. This thesis proposes methods that can overcome the problems of good behaviour selection and good behaviour deletion. It also addresses the problem of missing information, solves the problem of oscillating between correct and incorrect behaviours, and addresses the low efficiency in mapping the input to the correct behaviour. A Distributed Learning Classifier System (DLCS) consisting of five Learning Classifier Systems (LCS) with hierarchical architecture of three levels is used. An enhanced Bucket Brigade Algorithm (BBA) is developed to avoid the problem of choosing classifiers with high strength value but with incorrect behaviour. An approach that detects steady state value for calling genetic algorithm (GA) is proposed to overcome the problems of good classifiers deletion and the local minima trap. Finally, efficient solutions for covering detectors, supporting default hierarchies formation and the oscillation between correct and incorrect action are introduced to avoid performance failure, generalisation of classifiers that have the ability to cover the specific and general conditions, and loss of desirable classifiers respectively. Overall, the enhanced approaches performed well and the enhanced learning processes proposed in the current study makes robot learning more effective. The simulated robot is tested and results have shown that it performs better with the four basic behaviours. The simulated robot is also tested on many examples of a complex behaviour which is any combination of the four basic behaviours and the results have shown that it performs better with this type of behaviours as well. 2010-11 Thesis http://eprints.usm.my/41974/ http://eprints.usm.my/41974/1/SAEED_MOHAMMED_SAEED_BANEAMOON_HJ.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Baneamoon, Saeed Mohammed Saeed Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours
description	The four basic behaviours of mobile robot are chasing, approaching, avoiding and escaping. The main problem in robotic system is in selecting the correct behaviour. The aim of this research is to overcome the behaviour selection problem. This thesis proposes methods that can overcome the problems of good behaviour selection and good behaviour deletion. It also addresses the problem of missing information, solves the problem of oscillating between correct and incorrect behaviours, and addresses the low efficiency in mapping the input to the correct behaviour. A Distributed Learning Classifier System (DLCS) consisting of five Learning Classifier Systems (LCS) with hierarchical architecture of three levels is used. An enhanced Bucket Brigade Algorithm (BBA) is developed to avoid the problem of choosing classifiers with high strength value but with incorrect behaviour. An approach that detects steady state value for calling genetic algorithm (GA) is proposed to overcome the problems of good classifiers deletion and the local minima trap. Finally, efficient solutions for covering detectors, supporting default hierarchies formation and the oscillation between correct and incorrect action are introduced to avoid performance failure, generalisation of classifiers that have the ability to cover the specific and general conditions, and loss of desirable classifiers respectively. Overall, the enhanced approaches performed well and the enhanced learning processes proposed in the current study makes robot learning more effective. The simulated robot is tested and results have shown that it performs better with the four basic behaviours. The simulated robot is also tested on many examples of a complex behaviour which is any combination of the four basic behaviours and the results have shown that it performs better with this type of behaviours as well.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Baneamoon, Saeed Mohammed Saeed
author_facet	Baneamoon, Saeed Mohammed Saeed
author_sort	Baneamoon, Saeed Mohammed Saeed
title	Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours
title_short	Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours
title_full	Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours
title_fullStr	Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours
title_full_unstemmed	Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours
title_sort	enhanced distributed learning classifier system for simulated mobile robot behaviours
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2010
url	http://eprints.usm.my/41974/1/SAEED_MOHAMMED_SAEED_BANEAMOON_HJ.pdf
_version_	1747821006881292288

Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours

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