Classical conditioning in social robots
(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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- author
- Novianto, Rony ; Williams, Mary Anne ; Gärdenfors, Peter LU and Wightwick, Glenn
- organization
- publishing date
- 2014-01-01
- 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
- 2024-01-01 10:07:45
@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}},
}