Abstract
We study best-effort strategies (aka plans) in fully observable nondeterministic domains (FOND) for goals expressed in Linear Temporal Logic on Finite Traces (ltl \(_f\)). The notion of best-effort strategy has been introduced to also deal with the scenario when no agent strategy exists that fulfills the goal against every possible nondeterministic environment reaction. Such strategies fulfill the goal if possible, and do their best to do so otherwise. We present a technique for synthesizing best-effort strategies and propose some possible extensions of best-effort synthesis for multiple goal and planning domain specifications.
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Acknowledgements
We thank the contributions of all the co-authors (in alphabetical order): Benjamin Aminof, Giuseppe De Giacomo, Sasha Rubin, and Shufang Zhu. This work has been carried out while Gianmarco Parretti was enrolled in the Italian National Doctorate on Artificial Intelligence run by Sapienza University of Rome. This work has been partially supported by the ERC-ADG White- Mech (No. 834228), the EU ICT-48 2020 project TAILOR (No. 952215), the PRIN project RIPER (No. 20203FFYLK), and the PNRR MUR project FAIR (No. PE0000013).
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Parretti, G. (2023). ltl \(_f\) Best-Effort Synthesis for Single and Multiple Goal and Planning Domain Specifications. In: Malvone, V., Murano, A. (eds) Multi-Agent Systems. EUMAS 2023. Lecture Notes in Computer Science(), vol 14282. Springer, Cham. https://doi.org/10.1007/978-3-031-43264-4_40
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DOI: https://doi.org/10.1007/978-3-031-43264-4_40
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