Publication Details
Counterexample-Driven Synthesis for Probabilistic Program Sketches
probabilistic programs, synthesis, counter-examples, SMT solving
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 nite-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.
@inproceedings{BUT161455,
author="ČEŠKA, M. and HENSE, C. and JUNGES, S. and KATOEN, J.",
title="Counterexample-Driven Synthesis for Probabilistic Program Sketches",
booktitle="Proceedings of the 23rd International Symposium on Formal Methods.",
year="2019",
series="Lecture Notes of Computer Science",
pages="101--120",
publisher="Springer International Publishing",
address="Porto",
doi="10.1007/978-3-030-30942-8\{_}8",
isbn="978-3-030-30941-1"
}