Abstract
This paper focuses on improving the action plans obtained through the use of sequential macro-actions in temporal planning. Macro-actions are a way to address the high complexity of temporal planning in challenging domains. Investigating the Robocup Logistics League (RCLL), a testbed for logistics scenarios in the area of Industry 4.0, we introduce a method to unfold the macro-actions of an obtained abstract plan back into their original atomic actions in an improved plan. This allows to extract potentially better solutions in terms of makespan from the Simple Temporal Network (STN) representing the abstract plan. The proposed method is evaluated on a macro-based modeling of the RCLL domain and is shown to yield improved plans over those obtained using either the original atomic actions or the macro-actions without refinement.
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Acknowledgements
M. De Bortoli and M. Gebser were funded by Kärntner Wirtschaftsförderungs Fonds (project no. 28472), cms electronics GmbH, FunderMax GmbH, Hirsch Armbänder GmbH, incubed IT GmbH, Infineon Technologies Austria AG, Isovolta AG, Kostwein Holding GmbH, and Privatstiftung Kärntner Sparkasse. L. Chrpa was funded by the Czech Science Foundation (project no. 23-05575S). M. De Bortoli’s and M. Gebser’s visit to CTU in Prague was funded by the OP VVV project no. EF15_003/0000470 “Robotics 4 Industry 4.0” and by the Czech Ministry of Education, Youth and Sports under the Czech-Austrian Mobility programme (project no. 8J22AT003), respectively. L. Chrpa’s visits to University of Klagenfurt were funded by OeAD, Austria’s Agency for Education and Internationalisation (project no. CZ 15/2022).
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De Bortoli, M., Chrpa, L., Gebser, M., Steinbauer-Wagner, G. (2024). Improving Applicability of Planning in the RoboCup Logistics League Using Macro-actions Refinement. In: Buche, C., Rossi, A., Simões, M., Visser, U. (eds) RoboCup 2023: Robot World Cup XXVI. RoboCup 2023. Lecture Notes in Computer Science(), vol 14140. Springer, Cham. https://doi.org/10.1007/978-3-031-55015-7_24
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