Classical conditioning in social robots

Novianto, R; Williams, MA; Gärdenfors, P; Wightwick, G

Classical conditioning in social robots

Novianto, R

Williams, MA

Gärdenfors, P Wightwick, G

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8755 pp. 279 - 289
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Novianto, R https://orcid.org/0000-0002-2973-7669	en_US
dc.contributor.author	Williams, MA https://orcid.org/0000-0002-1047-0503	en_US
dc.contributor.author	Gärdenfors, P	en_US
dc.contributor.author	Wightwick, G https://orcid.org/0000-0001-6441-3179	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8755 pp. 279 - 289	en_US
dc.identifier.isbn	9783319119724	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35369
dc.identifier.uri	http://hdl.handle.net/10453/32240
dc.description.abstract	© Springer International Publishing Switzerland 2014. Classical conditioning is important in humans to learn and predict events in terms of associations between stimuli and to produce responses based on these associations. Social robots that have a classical conditioning skill like humans will have an advantage to interact with people more naturally, socially and effectively. In this paper, we present a novel classical conditioning mechanism and describe its implementation in ASMO cognitive architecture. The capability of this mechanism is demonstrated in the Smokey robot companion experiment. Results show that Smokey can associate stimuli and predict events in its surroundings. ASMO’s classical conditioning mechanism can be used in social robots to adapt to the environment and to improve the robots’ performances.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-11973-1_29	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Classical conditioning in social robots	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8755	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	8755	en_US

Abstract:

© Springer International Publishing Switzerland 2014. Classical conditioning is important in humans to learn and predict events in terms of associations between stimuli and to produce responses based on these associations. Social robots that have a classical conditioning skill like humans will have an advantage to interact with people more naturally, socially and effectively. In this paper, we present a novel classical conditioning mechanism and describe its implementation in ASMO cognitive architecture. The capability of this mechanism is demonstrated in the Smokey robot companion experiment. Results show that Smokey can associate stimuli and predict events in its surroundings. ASMO’s classical conditioning mechanism can be used in social robots to adapt to the environment and to improve the robots’ performances.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/32240