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Breaking Down High-Level Robot Path-Finding Abstractions in Natural Language Programming

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AIxIA 2020 – Advances in Artificial Intelligence (AIxIA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12414))

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Abstract

Natural language programming (NLPr) allows people to program in natural language (NL) for specific domains. It poses great potential since it gives non-experts the ability to develop projects without exhaustive training. However, complex descriptions can sometimes have multiple interpretations, making program synthesis difficult. Thus, if the high-level abstractions can be broken down into a sequence of precise low-level steps, existing natural language processing (NLP) and NLPr techniques could be adaptable to handle the tasks. In this paper, we present an algorithm for converting high-level task descriptions into low-level specifications by parsing the sentences into sentence frames and using generated low-level NL instructions to generate executable programs for pathfinding tasks in a LEGO Mindstorms EV3 robot. Our analysis shows that breaking down the high-level pathfinding abstractions into a sequence of low-level NL instructions is effective for the majority of collected sentences, and the generated NL texts are detailed, readable, and can easily be processed by the existing NLPr system.

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Notes

  1. 1.

    https://www.firstlegoleague.org/.

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Authors and Affiliations

  1. Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24060, USA

    Yue Zhan & Michael S. Hsiao

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  1. Yue Zhan
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  2. Michael S. Hsiao
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Correspondence to Yue Zhan .

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Editors and Affiliations

  1. Università degli Studi di Torino, Turin, Italy

    Matteo Baldoni

  2. Department of Informatics, Systems and C, University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

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Zhan, Y., Hsiao, M.S. (2021). Breaking Down High-Level Robot Path-Finding Abstractions in Natural Language Programming. In: Baldoni, M., Bandini, S. (eds) AIxIA 2020 – Advances in Artificial Intelligence. AIxIA 2020. Lecture Notes in Computer Science(), vol 12414. Springer, Cham. https://doi.org/10.1007/978-3-030-77091-4_18

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  • DOI: https://doi.org/10.1007/978-3-030-77091-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77090-7

  • Online ISBN: 978-3-030-77091-4

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