Abstract
We present PET, a specialized and highly optimized framework for partial exploration on probabilistic systems. Over the last decade, several significant advances in the analysis of Markov decision processes employed partial exploration. In a nutshell, this idea allows to focus computation on specific parts of the system, guided by heuristics, while maintaining correctness. In particular, only relevant parts of the system are constructed on demand, which in turn potentially allows to omit constructing large parts of the system. Depending on the model, this leads to dramatic speed-ups, in extreme cases even up to an arbitrary factor. PET unifies several previous implementations and provides a flexible framework to easily implement partial exploration for many further problems. Our experimental evaluation shows significant improvements compared to the previous implementations while vastly reducing the overhead required to add support for additional properties.
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Unfortunately, Java does not allow to specify the IEEE 754 rounding mode for floating point operations, which would further increase numerical stability, see [6].
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The implementation is not available from the URL mentioned in [3], we obtained the sources from the authors and include it in our artefact.
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Acknowledgements
We thank Pranav Ashok and Maximilian Weininger for their contributions to spiritual predecessors of PET as well as motivating the initial development of this tool.
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Meggendorfer, T. (2022). PET – A Partial Exploration Tool for Probabilistic Verification. In: Bouajjani, A., Holík, L., Wu, Z. (eds) Automated Technology for Verification and Analysis. ATVA 2022. Lecture Notes in Computer Science, vol 13505. Springer, Cham. https://doi.org/10.1007/978-3-031-19992-9_20
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