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Planning with Partial Observability by SAT
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en
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16
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Logics in Artificial Intelligence - 18th European Conference, JELIA 2023, Proceedings, pp. 605-620, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Volume 14281 LNAI
Abstract
Geffner & Geffner (2018) have shown that finding plans by reduction to SAT is not limited to classical planning, but is competitive also for fully observable non-deterministic planning. This work extends these ideas to planning with partial observability. Specifically, we handle partial observability by requiring that during the execution of a plan, the same actions have to be taken in all indistinguishable circumstances. We demonstrate that encoding this condition directly leads to far better scalability than an explicit encoding of observations-to-actions mapping, for high numbers of observations.
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Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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Fadnis, S & Rintanen, J 2023, Planning with Partial Observability by SAT. in S Gaggl, M V Martinez, M Ortiz & M Ortiz (eds), Logics in Artificial Intelligence - 18th European Conference, JELIA 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14281 LNAI, Springer, pp. 605-620, European Conference on Logics in Artificial Intelligence, Dresden, Germany, 20/09/2023. https://doi.org/10.1007/978-3-031-43619-2_41