2.50
Hdl Handle:
http://hdl.handle.net/10547/270600
Title:
Active robot learning for building up high-order beliefs
Authors:
Li, Dayou; Liu, Beisheng; Maple, Carsten; Jiang, Daming; Yue, Yong
Abstract:
High-order beliefs of service robots regard the robots' thought about their users' intention and preference. The existing approaches to the development of such beliefs through machine learning rely on particular social cues or specifically defined award functions. Their applications can, therefore, be limited. This paper presents an active robot learning approach to facilitate the robots to develop the beliefs by actively collecting/discovering evidence they need. The emphasis is on active learning. Hence social cues and award functions are not necessary. Simulations show that the presented approach successfully enabled a robot to discover evidences it needs.
Citation:
Dayou Li; Beisheng Liu; Maple, C.; Daming Jiang; Yong Yue; (2008) Active Robot Learning for Building Up High-Order Beliefs, Fuzzy Systems and Knowledge Discovery, FSKD '08, Fifth International Conference on , vol.3, pp.201-205
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date:
2008
URI:
http://hdl.handle.net/10547/270600
DOI:
10.1109/FSKD.2008.186
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4666240
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
9780769533056
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Dayouen_GB
dc.contributor.authorLiu, Beishengen_GB
dc.contributor.authorMaple, Carstenen_GB
dc.contributor.authorJiang, Damingen_GB
dc.contributor.authorYue, Yongen_GB
dc.date.accessioned2013-02-27T16:20:35Z-
dc.date.available2013-02-27T16:20:35Z-
dc.date.issued2008-
dc.identifier.citationDayou Li; Beisheng Liu; Maple, C.; Daming Jiang; Yong Yue; (2008) Active Robot Learning for Building Up High-Order Beliefs, Fuzzy Systems and Knowledge Discovery, FSKD '08, Fifth International Conference on , vol.3, pp.201-205en_GB
dc.identifier.isbn9780769533056-
dc.identifier.doi10.1109/FSKD.2008.186-
dc.identifier.urihttp://hdl.handle.net/10547/270600-
dc.description.abstractHigh-order beliefs of service robots regard the robots' thought about their users' intention and preference. The existing approaches to the development of such beliefs through machine learning rely on particular social cues or specifically defined award functions. Their applications can, therefore, be limited. This paper presents an active robot learning approach to facilitate the robots to develop the beliefs by actively collecting/discovering evidence they need. The emphasis is on active learning. Hence social cues and award functions are not necessary. Simulations show that the presented approach successfully enabled a robot to discover evidences it needs.en_GB
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4666240en_GB
dc.subjectactive learningen_GB
dc.subjectcognitive roboticsen_GB
dc.subjectfuzzy logicen_GB
dc.titleActive robot learning for building up high-order beliefsen
dc.typeConference papers, meetings and proceedingsen
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