Inductive Synthesis for Probabilistic Programs Reaches New Horizons

Lecture notes in computer science, pp. 191–209

Abstract

Abstract 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.

Authors 4

1. Roman Andriushchenko

   Brno University of Technology

   Affiliation as printed

   Brno University of Technology, Brno, Czech Republic

2. Milan Češka corresponding

   Brno University of Technology

   Affiliation as printed

   Brno University of Technology, Brno, Czech Republic

3. Sebastian Junges

   University of California, Berkeley

   Affiliation as printed

   University of California, Berkeley, USA

4. Joost-Pieter Katoen Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany