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Counterexample-Driven Synthesis for Probabilistic Program Sketches

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Formal Methods – The Next 30 Years (FM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11800))

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Abstract

Probabilistic programs are key to deal with uncertainty in, e.g., controller synthesis. They are typically small but intricate. Their development is complex and error prone requiring quantitative reasoning over a myriad of alternative designs. To mitigate this complexity, we adopt counterexample-guided inductive synthesis (CEGIS) to automatically synthesise finite-state probabilistic programs. Our approach leverages efficient model checking, modern SMT solving, and counterexample generation at program level. Experiments on practically relevant case studies show that design spaces with millions of candidate designs can be fully explored using a few thousand verification queries.

This work has been supported by the Czech Science Foundation grant No. GA19-24397S, the IT4Innovations excellence in science project No. LQ1602, the DFG RTG 2236 “UnRAVeL”, and the ERC Advanced Grant 787914 “FRAPPANT”,.

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Notes

  1. 1.

    We focus on program-level synthesis, and refrain from discussing important implementation aspects—like how to represent \(Q\)—here.

  2. 2.

    A good implementation takes a subset of \(B' \subseteq B\) by considering the \(\texttt {Prob}(D,\lozenge B')\).

  3. 3.

    As in Sect. 3.1: A partial realisation for \(\mathfrak {S}_H\) is a function \({\bar{R} :H \rightarrow \mathsf {Option}_H \cup \{\bot \}}\) s.t. \(\forall h\in H.~\bar{R}(h) \in \mathsf {Option}_h \cup {\{ \perp \}}\). For partial realisations \(\bar{R}_1, \bar{R}_2\), let \(\bar{R}_1 \subseteq \bar{R}_2\) iff \(\forall h \in H.\;\bar{R}_1(h) \in \{ \bar{R}_2(h),\perp \}\). Let R be a realisation s.t. \(\mathfrak {S}_H(R) \not \models \varphi \) for \(\varphi \in \varPhi \). Partial realisation \(\bar{R}_{\varphi } \subseteq R\) is a conflict for \(\varphi \) iff \(\forall R' \supseteq \bar{R}_{\varphi }~\mathfrak {S}_H(R') \not \models \varphi \).

  4. 4.

    https://github.com/moves-rwth/sketching.

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Authors and Affiliations

  1. Brno University of Technology, FIT, IT4I Centre of Excellence, Brno, Czech Republic

    Milan Češka

  2. RWTH Aachen University, Aachen, Germany

    Christian Hensel, Sebastian Junges & Joost-Pieter Katoen

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  1. Milan Češka
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  2. Christian Hensel
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  1. Consiglio Nazionale delle Ricerche, Pisa, Italy

    Maurice H. ter Beek

  2. Macquarie University, Sydney, NSW, Australia

    Annabelle McIver

  3. University of Minho, Braga, Portugal

    José N. Oliveira

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Češka, M., Hensel, C., Junges, S., Katoen, JP. (2019). Counterexample-Driven Synthesis for Probabilistic Program Sketches. In: ter Beek, M., McIver, A., Oliveira, J. (eds) Formal Methods – The Next 30 Years. FM 2019. Lecture Notes in Computer Science(), vol 11800. Springer, Cham. https://doi.org/10.1007/978-3-030-30942-8_8

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