Publication Details
Inductive Synthesis for Probabilistic Programs Reaches New Horizons
Probabilistic programs, Inductive Synthesis, Counterexamples, Probabilistic Model
Checking
This paper presents a novel method for the automated synthesis of probabilistic
programs. The starting point is a program sketch representing a finite family of
finite-state Markov chains with related but distinct topologies, and
a reachability specification. The method builds on a novel inductive oracle that
greedily generates counter-examples (CEs) for violating programs and uses them to
prune the family. These CEs leverage the semantics of the family in the form of
bounds on its best- and worst-case behaviour provided by a deductive oracle using
an MDP abstraction. The method further monitors the performance of the synthesis
and adaptively switches between inductive and deductive reasoning. Our
experiments demonstrate that the novel CE construction provides a significantly
faster and more effective pruning strategy leading to an accelerated synthesis
process on a wide range of benchmarks. For challenging problems, such as the
synthesis of decentralized partially-observable controllers, we reduce the
run-time from a day to minutes.
@inproceedings{BUT171484,
author="ANDRIUSHCHENKO, R. and ČEŠKA, M. and JUNGES, S. and KATOEN, J.",
title="Inductive Synthesis for Probabilistic Programs Reaches New Horizons",
booktitle="International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS)",
year="2021",
series="Lecture Notes in Computer Science",
pages="191--209",
publisher="Springer International Publishing",
address="Cham",
doi="10.1007/978-3-030-72016-2\{_}11",
isbn="978-3-030-72015-5"
}