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 finite-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.
CITATION STYLE
Češka, M., Hensel, C., Junges, S., & Katoen, J. P. (2019). Counterexample-driven synthesis for probabilistic program sketches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11800 LNCS, pp. 101–120). Springer. https://doi.org/10.1007/978-3-030-30942-8_8
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