ML Anthology

Finite-State Controllers of POMDPs Using Parameter Synthesis

Sebastian Junges, Nils Jansen, Ralf Wimmer, Tim Quatmann, Leonore Winterer, Joost-Pieter Katoen, Bernd Becker
UAI 2018 pp. 519-529
/uai/2018/junges2018uai-finite/

Abstract

We study finite-state controllers (FSCs) for partially observable Markov decision processes (POMDPs) that are provably correct with respect to given specifications. The key insight is that computing (randomised) FSCs on POMDPs is equivalent to---and computationally as hard as---synthesis for parametric Markov chains (pMCs). This correspondence 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 performance to well-known POMDP solvers.

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Text

Junges et al. "Finite-State Controllers of POMDPs Using Parameter Synthesis." Conference on Uncertainty in Artificial Intelligence, 2018.

Markdown

[Junges et al. "Finite-State Controllers of POMDPs Using Parameter Synthesis." Conference on Uncertainty in Artificial Intelligence, 2018.](https://mlanthology.org/uai/2018/junges2018uai-finite/)

BibTeX

@inproceedings{junges2018uai-finite,
  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 = {Conference on Uncertainty in Artificial Intelligence},
  year      = {2018},
  pages     = {519-529},
  url       = {https://mlanthology.org/uai/2018/junges2018uai-finite/}
}