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dc.contributor.authorLee, WPen_US
dc.date.accessioned2014-12-08T15:45:58Z-
dc.date.available2014-12-08T15:45:58Z-
dc.date.issued1999-12-01en_US
dc.identifier.issn0020-0255en_US
dc.identifier.urihttp://dx.doi.org/10.1016/S0020-0255(99)00078-Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/30926-
dc.description.abstractBuilding robots is a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to such difficulties in designing robots is to adopt learning methods. The evolution-based approach is a special method of machine learning and it has been advocated to automate the design of robots. Yet, the tasks achieved so far are fairly simple. In this work, we first analyze the difficulties of applying evolutionary approaches to synthesize robot controllers for complicated tasks, and then suggest an approach to resolve them. Instead of directly evolving a monolithic control system, we propose to decompose the overall task to fit in the behavior-based control architecture, and then to evolve the separate behavior modules and arbitrators using an evolutionary approach. Consequently, the job of defining fitness functions becomes more straightforward and the tasks easier to achieve. To assess the performance of the developed approach, we evolve a control system to achieve an application task of box-pushing as an example. Experimental results show the promise and efficiency of the presented approach. (C) 1999 Elsevier Science Inc. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectevolutionary computingen_US
dc.subjectgenetic programmingen_US
dc.subjectcomputational intelligenceen_US
dc.subjectrobot learningen_US
dc.subjectautomatic robot programmingen_US
dc.titleEvolving complex robot behaviorsen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/S0020-0255(99)00078-Xen_US
dc.identifier.journalINFORMATION SCIENCESen_US
dc.citation.volume121en_US
dc.citation.issue1-2en_US
dc.citation.spage1en_US
dc.citation.epage25en_US
dc.contributor.department友訊交大聯合研發中心zh_TW
dc.contributor.departmentD Link NCTU Joint Res Ctren_US
dc.identifier.wosnumberWOS:000084329600001-
dc.citation.woscount15-
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