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Conference Paper: Response knowledge learning of autonomous agent

TitleResponse knowledge learning of autonomous agent
Authors
Issue Date2004
PublisherInstitute of Electrical and Electronics Engineers. The Journals web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500
Citation
The 2004 IEEE International Joint Conference on Neural Networks (IJCNN 2004), Budapest, Hungary, 25-29 July 2004. In IEEE International Conference on Neural Networks Proceedings, 2004, v. 2, p. 1133-1138 How to Cite?
AbstractIn robot applications, the performance of a robot agent is measured by the quantity of award received from its response. Many literatures [1-5] define the response as either a state diagram or a neural network. Due to the absence of a desired response, neither of them is applicable to an unstructural environment. In this paper, a novel Response Knowledge Learning algorithm is proposed to handle this domain. By using a set of experiences, the algorithm can extract the contributed experiences to construct the response function. Two sets of environments are provided to illustrate the performance of the proposed algorithm. The results show that it can effectively construct the response function that receives an award which is very close to the true maximum.
Persistent Identifierhttp://hdl.handle.net/10722/196646
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChow, C-K-
dc.contributor.authorTsui, H-T-
dc.date.accessioned2014-04-24T02:10:30Z-
dc.date.available2014-04-24T02:10:30Z-
dc.date.issued2004-
dc.identifier.citationThe 2004 IEEE International Joint Conference on Neural Networks (IJCNN 2004), Budapest, Hungary, 25-29 July 2004. In IEEE International Conference on Neural Networks Proceedings, 2004, v. 2, p. 1133-1138-
dc.identifier.isbn0-7803-8359-1-
dc.identifier.issn1098-7576-
dc.identifier.urihttp://hdl.handle.net/10722/196646-
dc.description.abstractIn robot applications, the performance of a robot agent is measured by the quantity of award received from its response. Many literatures [1-5] define the response as either a state diagram or a neural network. Due to the absence of a desired response, neither of them is applicable to an unstructural environment. In this paper, a novel Response Knowledge Learning algorithm is proposed to handle this domain. By using a set of experiences, the algorithm can extract the contributed experiences to construct the response function. Two sets of environments are provided to illustrate the performance of the proposed algorithm. The results show that it can effectively construct the response function that receives an award which is very close to the true maximum.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journals web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500-
dc.relation.ispartofIEEE International Conference on Neural Networks Proceedings-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE International Conference on Neural Networks Proceedings. Copyright © Institute of Electrical and Electronics Engineers.-
dc.titleResponse knowledge learning of autonomous agent-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/IJCNN.2004.1380094-
dc.identifier.scopuseid_2-s2.0-10944254097-
dc.identifier.volume2-
dc.identifier.spage1133-
dc.identifier.epage1138-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160603 amended-

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