Publication Details
PAYNT: A Tool for Inductive Synthesis of Probabilistic Programs
Češka Milan, doc. RNDr., Ph.D. (DITS FIT BUT)
Junges Sebastian (RWTH Aachen University)
Katoen Joost-Pieter (RWTH)
Stupinský Šimon, Ing. ()
Probabilistic programs,
Inductive Synthesis,
Counterexamples,
Probabilistic Model Checking
This paper presents PAYNT, a tool to automatically synthesise probabilistic programs. PAYNT enables the synthesis of finite-state probabilistic programs from a program sketch representing a finite family of program candidates. A tight interaction between inductive oracle-guided methods with state-of-the-art probabilistic model checking is at the heart of PAYNT. These oracle-guided methods effectively reason about all possible candidates and synthesise programs that meet a given specification formulated as a conjunction of temporal logic constraints and possibly including an optimising objective. We demonstrate the performance and usefulness of PAYNT using several case studies from different application domains; e.g., we find the optimal randomized protocol for network stabilisation among 3M potential programs within minutes, whereas alternative approaches would need days to do so.
@INPROCEEDINGS{FITPUB12533, author = "Roman Andriushchenko and Milan \v{C}e\v{s}ka and Sebastian Junges and Joost-Pieter Katoen and \v{S}imon Stupinsk\'{y}", title = "PAYNT: A Tool for Inductive Synthesis of Probabilistic Programs", pages = "856--869", booktitle = "International Conference on Computer Aided Verification (CAV)", series = "Lecture Notes in Computer Science", volume = 12759, year = 2021, location = "Cham, DE", publisher = "Springer Verlag", ISBN = "978-3-030-81684-1", doi = "10.1007/978-3-030-81685-8\_40", language = "english", url = "https://www.fit.vut.cz/research/publication/12533" }