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Classical conditioning in social robots

Novianto, Rony ; Williams, Mary Anne ; Gärdenfors, Peter LU and Wightwick, Glenn (2014) 6th International Conference on Social Robotics, ICSR 2014 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8755. p.279-289
Abstract

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... (More)

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.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
ASMO cognitive architecture, Classical conditioning, Maximum likelihood estimation
host publication
Social Robotics : 6th International Conference, ICSR 2014, Proceedings - 6th International Conference, ICSR 2014, Proceedings
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
editor
Beetz, Michael ; Beetz, Michael ; Williams, Mary-Anne ; Johnston, Benjamin and Williams, Mary-Anne
volume
8755
pages
279 - 289
publisher
Springer
conference name
6th International Conference on Social Robotics, ICSR 2014
conference location
Sydney, Australia
conference dates
2014-10-27 - 2014-10-29
external identifiers
  • scopus:84910007689
ISSN
0302-9743
1611-3349
ISBN
9783319119724
DOI
10.1007/978-3-319-11973-1_29
language
English
LU publication?
yes
id
bb536251-14ea-4a3e-9624-83449be58432
date added to LUP
2019-06-12 16:39:51
date last changed
2025-01-09 13:59:55
@inproceedings{bb536251-14ea-4a3e-9624-83449be58432,
  abstract     = {{<p>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.</p>}},
  author       = {{Novianto, Rony and Williams, Mary Anne and Gärdenfors, Peter and Wightwick, Glenn}},
  booktitle    = {{Social Robotics : 6th International Conference, ICSR 2014, Proceedings}},
  editor       = {{Beetz, Michael and Beetz, Michael and Williams, Mary-Anne and Johnston, Benjamin and Williams, Mary-Anne}},
  isbn         = {{9783319119724}},
  issn         = {{0302-9743}},
  keywords     = {{ASMO cognitive architecture; Classical conditioning; Maximum likelihood estimation}},
  language     = {{eng}},
  month        = {{01}},
  pages        = {{279--289}},
  publisher    = {{Springer}},
  series       = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
  title        = {{Classical conditioning in social robots}},
  url          = {{http://dx.doi.org/10.1007/978-3-319-11973-1_29}},
  doi          = {{10.1007/978-3-319-11973-1_29}},
  volume       = {{8755}},
  year         = {{2014}},
}