Inductive Synthesis for Probabilistic Programs Reaches New Horizons
This addresses the problem of efficient probabilistic program synthesis for researchers and practitioners in formal methods and AI, representing a strong incremental advance with specific performance gains.
The paper tackles the automated synthesis of probabilistic programs by introducing a novel inductive oracle that generates counter-examples to prune program families, resulting in significant speed-ups, such as reducing run-time from a day to minutes for decentralized partially-observable controllers.
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 PCTL 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 the inductive and deductive reasoning. Our experiments demonstrate that the novel CE construction provides a significantly faster and more effective pruning strategy leading to acceleration of the 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.