# 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…

## 16 Citations

Model Repair Revamped: On the Automated Synthesis of Markov Chains

- Computer Science
- 2021

This paper outlines two approaches—based on counterexampleguided abstraction refinement (CEGAR) and counterexample-guided inductive synthesis (CEGIS), respectively—to the automated synthesis of…

Model Repair Revamped - - On the Automated Synthesis of Markov Chains -

- Computer ScienceFrom Reactive Systems to Cyber-Physical Systems
- 2019

Two approaches to the automated synthesis of finite-state probabilistic models and programs based on counterexample-guided abstraction refinement (CEGAR andCEGIS) are outlined and the applicability of these synthesis techniques to sketching of Probabilistic programs, controller synthesis of POMDPs, and software product lines is shown.

Inductive Synthesis for Probabilistic Programs Reaches New Horizons

- Computer ScienceTACAS
- 2021

A novel inductive oracle that greedily generates counter-examples for violating programs and uses them to prune the family to provide a significantly faster and more effective pruning strategy leading to an accelerated synthesis process on a wide range of benchmarks.

Counterexample-guided inductive synthesis for probabilistic systems

- Computer ScienceFormal Aspects Comput.
- 2021

The crux is to aggressively prune the search space by using counterexamples provided by a Probabilistic model checker to automatically synthesise probabilistic models.

Minimal witnesses for probabilistic timed automata

- Computer Science, MathematicsATVA
- 2020

Using a new operation on difference bounds matrices, it is shown how Farkas certificates of finite-state bisimulation quotients of a PTA can be translated into witnessing subsystems.

On the Complexity of Reachability in Parametric Markov Decision Processes

- Mathematics, Computer ScienceCONCUR
- 2019

The complexity of finding values for parameters such that the induced MDP satisfies some reachability constraints is studied, and all known lower bounds are improved and ETR-completeness results for distinct variants of this problem are provided.

Parameter Synthesis in Markov Models: A Gentle Survey

- Computer ScienceArXiv
- 2022

The main ideas underlying state-of-the-art algorithms that established an impressive leap over the last decade enabling the fully automated analysis of models with millions of states and thousands of parameters are described.

Witnessing subsystems for probabilistic systems with low tree width

- Computer Science, MathematicsElectronic Proceedings in Theoretical Computer Science
- 2021

An algorithm which aims to utilise a given directed tree partition of the system to compute a minimal witnessing subsystem is described and it outperforms other approaches on certain benchmarks which have directed tree partitions of small width.

Farkas Certificates and Minimal Witnesses for Probabilistic Reachability Constraints

- Computer ScienceTACAS
- 2020

Farkas certificates for lower and upper bounds on minimal and maximal reachability probabilities in Markov decision processes (MDP) are introduced using an MDP-variant of Farkas’ Lemma.

Parameter Synthesis for Markov Models

- Computer ScienceArXiv
- 2019

This paper presents various analysis algorithms for parametric Markov chains and Markov decision processes, and gives a detailed account of the various algorithms, presents a software tool realising these techniques, and reports on an extensive experimental evaluation on benchmarks.

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