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  • GRAVITAS: A model checking based planning and goal reasoning framework for autonomous systems

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    Embargoed until: 2022-11-24
    File version
    Accepted Manuscript (AM)
    Author(s)
    Hou, Zhe
    Griffith University Author(s)
    Hou, Zhe
    Dong, Jin-Song
    Bride, Hadrien J.
    Year published
    2021
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    Abstract
    This work follow the verification as planning paradigm and propose to use model-checking techniques to solve planning and goal reasoning problems for autonomous systems with high-degree of assurance. It presents a novel modelling framework — Goal Task Network (GTN) that encompass both goal reasoning and planning under a unified formal description that enables the use of assurance tools. The paper provides a systematic method that highlights how an industrial model checker (PAT) can be used to solve goal reasoning and planning problems modelled by GTNs. Further, this paper also introduces the design of an automated system ...
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    This work follow the verification as planning paradigm and propose to use model-checking techniques to solve planning and goal reasoning problems for autonomous systems with high-degree of assurance. It presents a novel modelling framework — Goal Task Network (GTN) that encompass both goal reasoning and planning under a unified formal description that enables the use of assurance tools. The paper provides a systematic method that highlights how an industrial model checker (PAT) can be used to solve goal reasoning and planning problems modelled by GTNs. Further, this paper also introduces the design of an automated system framework for Goal Reasoning And Verification for Independent Trusted Autonomous Systems (GRAVITAS). The proposed framework is demonstrated in an experiment that simulates a survey mission performed by the REMUS-100 autonomous underwater vehicle.
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    Journal Title
    Engineering Applications of Artificial Intelligence
    Volume
    97
    DOI
    https://doi.org/10.1016/j.engappai.2020.104091
    Copyright Statement
    © 2021 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Artificial Intelligence and Image Processing
    Information and Computing Sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/403384
    Collection
    • Journal articles

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