Classical conditioning in social robots

Rony Novianto, Mary Anne Williams, Peter Gärdenfors, Glenn Wightwick

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingResearchpeer-review

3 Citations (SciVal)

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 the robots’ performances.

Original languageEnglish
Title of host publicationSocial Robotics
Subtitle of host publication6th International Conference, ICSR 2014, Proceedings
EditorsMichael Beetz, Michael Beetz, Mary-Anne Williams, Benjamin Johnston, Mary-Anne Williams
Place of PublicationCham
PublisherSpringer
Pages279-289
ISBN (Electronic)9783319119724
DOIs
Publication statusPublished - 2014 Jan 1
Event6th International Conference on Social Robotics, ICSR 2014 - Sydney, Australia
Duration: 2014 Oct 272014 Oct 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8755
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Social Robotics, ICSR 2014
Country/TerritoryAustralia
CitySydney
Period2014/10/272014/10/29

Subject classification (UKÄ)

  • Computer Vision and Robotics (Autonomous Systems)
  • Human Computer Interaction

Keywords

  • ASMO cognitive architecture
  • Classical conditioning
  • Maximum likelihood estimation

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