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Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes

Authors:

Jun TaniAuthors Info & Claims

Adaptive Behavior, Volume 13, Issue 1

Pages 33 - 52

https://doi.org/10.1177/105971230501300102

Published: 01 March 2005 Publication History

Abstract

We present a novel connectionist model for acquiring the semantics of a simple language through the behavioral experiences of a real robot. We focus on the "compositionality" of semantics and examine how it can be generated through experiments. Our experimental results showed that the essential structures for situated semantics can self-organize themselves through dense interactions between linguistic and behavioral processes whereby a certain generalization in learning is achieved. Our analysis of the acquired dynamical structures indicates that an equivalence of compositionality appears in the combinatorial mechanics self-organized in the neuronal nonlinear dynamics. The manner in which this mechanism of compositionality, based on dynamical systems, differs from that considered in conventional linguistics and other synthetic computational models, is discussed in this paper.

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Cited By

Tani JWhite J(2022)Cognitive neurorobotics and self in the shared world, a focused review of ongoing researchAdaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems10.1177/105971232096215830:1(81-100)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1177/1059712320962158
Karch TTeodorescu LHofmann KMoulin-Frier COudeyer PRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Grounding spatio-temporal language with transformersProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3540662(5236-5249)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3540662
Nanayakkara TBarfoot THoward TShao LMigimatsu TZhang QYang KBohg J(2021)Concept2RobotInternational Journal of Robotics Research10.1177/0278364921104628540:12-14(1419-1434)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1177/02783649211046285
Show More Cited By

Index Terms

Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. External interfaces for robotics
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Discourse, dialogue and pragmatics

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Information & Contributors

Information

Published In

cover image Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems Volume 13, Issue 1

March 2005

76 pages

ISSN:1059-7123

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Publisher

Sage Publications, Inc.

United States

Publication History

Published: 01 March 2005

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Cited By

Tani JWhite J(2022)Cognitive neurorobotics and self in the shared world, a focused review of ongoing researchAdaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems10.1177/105971232096215830:1(81-100)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1177/1059712320962158
Karch TTeodorescu LHofmann KMoulin-Frier COudeyer PRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Grounding spatio-temporal language with transformersProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3540662(5236-5249)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3540662
Nanayakkara TBarfoot THoward TShao LMigimatsu TZhang QYang KBohg J(2021)Concept2RobotInternational Journal of Robotics Research10.1177/0278364921104628540:12-14(1419-1434)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1177/02783649211046285
Goutsu YInamura T(2021)Linguistic Descriptions of Human Motion with Generative Adversarial Seq2Seq Learning2021 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA48506.2021.9561519(4281-4287)Online publication date: 30-May-2021
https://dl.acm.org/doi/10.1109/ICRA48506.2021.9561519
Förster FSaunders JLehmann HNehaniv C(2019)Robots Learning to Say “No”ACM Transactions on Human-Robot Interaction10.1145/33596188:4(1-26)Online publication date: 15-Nov-2019
https://dl.acm.org/doi/10.1145/3359618
Takano WTakahashi TNakamura Y(2019)Sequential Monte Carlo controller that integrates physical consistency and motion knowledgeAutonomous Robots10.1007/s10514-018-9815-543:6(1523-1536)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10514-018-9815-5
Zhong JPeniak MTani JOgata TCangelosi A(2019)Sensorimotor input as a language generalisation toolAutonomous Robots10.1007/s10514-018-9793-743:5(1271-1290)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10514-018-9793-7
Zhong JCangelosi AOgata TZhang XZhou Y(2018)Encoding Longer-Term Contextual Information with Predictive Coding and Ego-MotionComplexity10.1155/2018/76095872018Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1155/2018/7609587
Goutsu YTakano WNakamura Y(2018)Classification of Multi-class Daily Human Motion using Discriminative Body Parts and Sentence DescriptionsInternational Journal of Computer Vision10.1007/s11263-017-1053-3126:5(495-514)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s11263-017-1053-3
Takano WYamada YNakamura Y(2017)Generation of action description from classification of motion and objectRobotics and Autonomous Systems10.1016/j.robot.2017.02.00391:C(247-257)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1016/j.robot.2017.02.003
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