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Plan Generation via Behavior Trees Obtained from Goal-Oriented LTLf Formulas

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Autonomous Agents and Multiagent Systems. Best and Visionary Papers (AAMAS 2023)

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Abstract

Temporal logic can be used to formally specify autonomous agent goals, but synthesizing planners that guarantee goal satisfaction can be computationally prohibitive. This paper shows how to turn goals specified using a subset of finite trace Linear Temporal Logic (\(LTL_{f}\)) into a behavior tree (BT) that guarantees that successful traces satisfy the \(LTL_{f}\) goal. Useful \(LTL_{f}\) formulas for achievement goals can be derived using achievement-oriented task mission grammars, leading to missions made up of tasks combined using LTL operators. Constructing BTs from \(LTL_{f}\) formulas leads to a relaxed behavior synthesis problem in which a wide range of planners can implement the action nodes in the BT. Importantly, any successful trace induced by the planners satisfies the corresponding \(LTL_{f}\) formula. The usefulness of the approach is demonstrated in two ways: a) exploring the alignment between two planners and \(LTL_{f}\) goals, and b) solving a sequential key-door problem for a Fetch robot.

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Acknowledgements

This work was supported by the U.S. Office of Naval Research (N00014-18-1-2831). The authors thank Elijah Pettitt, who was an undergraduate research assistant, for programming and running the experiments with the Fetch robot.

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Authors and Affiliations

  1. Brigham Young University, Provo, UT, 84602, USA

    Aadesh Neupane, Michael A. Goodrich & Eric G. Mercer

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  1. Aadesh Neupane
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  2. Michael A. Goodrich
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  3. Eric G. Mercer
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Correspondence to Aadesh Neupane .

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Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Francesco Amigoni

  2. Rutgers University, Piscataway, NJ, USA

    Arunesh Sinha

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Neupane, A., Goodrich, M.A., Mercer, E.G. (2024). Plan Generation via Behavior Trees Obtained from Goal-Oriented LTLf Formulas. In: Amigoni, F., Sinha, A. (eds) Autonomous Agents and Multiagent Systems. Best and Visionary Papers. AAMAS 2023. Lecture Notes in Computer Science(), vol 14456. Springer, Cham. https://doi.org/10.1007/978-3-031-56255-6_6

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  • DOI: https://doi.org/10.1007/978-3-031-56255-6_6

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  • Online ISBN: 978-3-031-56255-6

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