Syntéza pravděpodobnostních programů s optimální cenou

This thesis pursues the synthesis of probabilistic programs with rewards. Probabilistic synthesis leads to the automatic proposal of a system which fulfills required specifications. In this thesis, I work with a form of synthesis where we have a sketch of a given system. This sketch includes unknown variables and the objective is to find a combination of configuration of given variables in order to have the final program meeting the specified requirements. Recently, new approaches considering a set of solutions as a family of Markov chain have appeared. One of these approaches is the usage of a new method combining counterexample-guided abstraction refinement and counterexample-guided inductive synthesis. This method exceeds other methods for synthesis of probabilistic programs with its efficiency. In this thesis, I concretely focus on extending this tool's specification language by adding a possibility of application of rewards and until properties. Thanks to these extensions it is possible to specify searched solution more efficiently. Experiments demonstrate that even after the addition of these possibilities of specifications the speed of a given tool remains by a margin of order of magnitudes more effective than the standard method of synthesis.

Synthesis of probabilistic models, Markov models