Dynamical linking of positive and negative sentences to goal-oriented robot behavior by hierarchical RNN

Tatsuro Yamada, Shingo Murata, Hiroaki Arie, Tetsuya Ogata

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    Meanings of language expressions are constructed not only from words grounded in real-world matters, but also from words such as “not” that participate in the construction by working as logical operators. This study proposes a connectionist method for learning and internally representing functions that deal with both of these word groups, and grounding sentences constructed from them in corresponding behaviors just by experiencing raw sequential data of an imposed task. In the experiment, a robot implemented with a recurrent neural network is required to ground imperative positive and negative sentences given as a sequence of words in corresponding goal-oriented behavior. Analysis of the internal representations reveals that the network fulfilled the requirement by extracting XOR problems implicitly included in the target sequences and solving them by learning to represent the logical operations in its nonlinear dynamics in a self-organizing manner.

    Original languageEnglish
    Title of host publicationArtificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
    PublisherSpringer Verlag
    Pages339-346
    Number of pages8
    Volume9886 LNCS
    ISBN (Print)9783319447773
    DOIs
    Publication statusPublished - 2016
    Event25th International Conference on Artificial Neural Networks, ICANN 2016 - Barcelona, Spain
    Duration: 2016 Sep 62016 Sep 9

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9886 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other25th International Conference on Artificial Neural Networks, ICANN 2016
    CountrySpain
    CityBarcelona
    Period16/9/616/9/9

    Keywords

    • Human–robot interaction
    • Logical operation
    • Recurrent neural network
    • Symbol grounding

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Yamada, T., Murata, S., Arie, H., & Ogata, T. (2016). Dynamical linking of positive and negative sentences to goal-oriented robot behavior by hierarchical RNN. In Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings (Vol. 9886 LNCS, pp. 339-346). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9886 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-44778-0_40