Finite-State Controllers of POMDPs using Parameter Synthesis

Sebastian Junges, Nils Jansen, Ralf Wimmer, Tim Quatmann, Leonore Winterer, Joost-Pieter Katoen, Bernd Becker
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:518-528, 2018.

Abstract

We study finite-state controllers (FSCs) for par- tially observable Markov decision processes (POMDPs) that are provably correct with re- spect to given specifications. The key in- sight is that computing (randomised) FSCs on POMDPs is equivalent to—and compu- tationally as hard as—synthesis for paramet- ric Markov chains (pMCs). This correspon- dence allows to use tools for synthesis in pMCs to compute correct-by-construction FSCs on POMDPs for a variety of specifications. Our experimental evaluation shows comparable per- formance to well-known POMDP solvers.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-junges18a, title = {Finite-State Controllers of POMDPs using Parameter Synthesis}, author = {Junges, Sebastian and Jansen, Nils and Wimmer, Ralf and Quatmann, Tim and Winterer, Leonore and Katoen, Joost-Pieter and Becker, Bernd}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {518--528}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/junges18a/junges18a.pdf}, url = {https://proceedings.mlr.press/r16/junges18a.html}, abstract = {We study finite-state controllers (FSCs) for par- tially observable Markov decision processes (POMDPs) that are provably correct with re- spect to given specifications. The key in- sight is that computing (randomised) FSCs on POMDPs is equivalent to—and compu- tationally as hard as—synthesis for paramet- ric Markov chains (pMCs). This correspon- dence allows to use tools for synthesis in pMCs to compute correct-by-construction FSCs on POMDPs for a variety of specifications. Our experimental evaluation shows comparable per- formance to well-known POMDP solvers.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Finite-State Controllers of POMDPs using Parameter Synthesis %A Sebastian Junges %A Nils Jansen %A Ralf Wimmer %A Tim Quatmann %A Leonore Winterer %A Joost-Pieter Katoen %A Bernd Becker %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-junges18a %I PMLR %P 518--528 %U https://proceedings.mlr.press/r16/junges18a.html %V R16 %X We study finite-state controllers (FSCs) for par- tially observable Markov decision processes (POMDPs) that are provably correct with re- spect to given specifications. The key in- sight is that computing (randomised) FSCs on POMDPs is equivalent to—and compu- tationally as hard as—synthesis for paramet- ric Markov chains (pMCs). This correspon- dence allows to use tools for synthesis in pMCs to compute correct-by-construction FSCs on POMDPs for a variety of specifications. Our experimental evaluation shows comparable per- formance to well-known POMDP solvers. %Z Reissued by PMLR on 04 October 2026.
APA
Junges, S., Jansen, N., Wimmer, R., Quatmann, T., Winterer, L., Katoen, J. & Becker, B.. (2018). Finite-State Controllers of POMDPs using Parameter Synthesis. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:518-528 Available from https://proceedings.mlr.press/r16/junges18a.html. Reissued by PMLR on 04 October 2026.

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