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

Tatsuro Yamada, Shingo Murata, Hiroaki Arie, Tetsuya Ogata*

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
編集者Alessandro E.P. Villa, Paolo Masulli, Antonio Javier Pons Rivero
出版社Springer Verlag
ページ339-346
ページ数8
ISBN(印刷版)9783319447773
DOI
出版ステータスPublished - 2016
イベント25th International Conference on Artificial Neural Networks, ICANN 2016 - Barcelona, Spain
継続期間: 2016 9 62016 9 9

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9886 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other25th International Conference on Artificial Neural Networks, ICANN 2016
国/地域Spain
CityBarcelona
Period16/9/616/9/9

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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