Counterexample-Driven Synthesis for Probabilistic Program Sketches

@inproceedings{Ceska2019CounterexampleDrivenSF,
  title={Counterexample-Driven Synthesis for Probabilistic Program Sketches},
  author={Milan Ceska and Christian Hensel and Sebastian Junges and Joost-Pieter Katoen},
  booktitle={FM},
  year={2019}
}
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… 
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