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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Notes
- 1.
In NNF, a formula contains only connectives \(\vee \), \(\wedge \) and \(\lnot \), and all negations \(\lnot \) are directly in front of an atomic proposition.
- 2.
To encode the constraints exactly-one and at-most-one for \(\phi _1,\ldots ,\phi _k\), we use the quadratic encoding \(\lnot \phi _i\vee \lnot \phi _j\) for all \(1\le i<j\le k\). Better encodings exist [18].
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Fadnis, S., Rintanen, J. (2023). Planning with Partial Observability by SAT. In: Gaggl, S., Martinez, M.V., Ortiz, M. (eds) Logics in Artificial Intelligence. JELIA 2023. Lecture Notes in Computer Science(), vol 14281. Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_41
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