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Generation of Plausible Incident Stories by Using Recurrent Neural Networks

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Advances in Artificial Intelligence, Software and Systems Engineering (AHFE 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1213))

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Abstract

Recent artificial intelligence (AI) technology is capable to generate new texts (so that it makes new stories) by learning order of words in particular datasets. This paper reports an experiment of generation of imaginal incident reports by AI text generator, which learn from real aviation incident reports. AI tries to synthesize new texts to maintain similarity of word order pattern to training data of real reports, while it may generate brand-new stories due to randomness in its algorithm. We may find hidden risk of human error incidents among those imaginal reports. Although theoretical capability of AI text generation is widely approved, it is still a practically hard problem, which requires large costs and massive amount of data. Therefore, the main contribution of this paper would be not only qualitative evaluation of made imaginal reports, but also quantitative evaluation of its feasibility.

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References

  1. NASA, Aviation Safety Reporting System Database. https://asrs.arc.nasa.gov/

  2. Bokka, K.R., Hora, S., Jain, T., Wambugu, M.: Deep Learning for Natural Language Processing. Packt Publishing Ltd, Birmingham (2019)

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Author information

Authors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology, Aomi 2-4-7, Koto, Tokyo, 135-0064, Japan

    Toru Nakata

Authors
  1. Toru Nakata
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Correspondence to Toru Nakata .

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Editors and Affiliations

  1. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Tareq Ahram

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Nakata, T. (2021). Generation of Plausible Incident Stories by Using Recurrent Neural Networks. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_44

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