Publications
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2025
ANDRIUSHCHENKO, R.; ČEŠKA, M.; MACÁK, F.; JUNGES, S. Policies Grow on Trees: Model Checking Families of MDPs. Proceeding of 22nd International Symposium on Automated Technology for Verification and Analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer Verlag, 2025.
p. 51-75. ISBN: 978-3-031-78749-2. DetailMACÁK, F.; ANDRIUSHCHENKO, R.; ČEŠKA, M.; JUNGES, S.; KATOEN, J. An Oracle-Guided Approach to Constrained Policy Synthesis Under Uncertainty. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2025, vol. 2025, no. 82,
p. 433-469. ISSN: 1076-9757. Detail -
2024
ČEŠKA, M.; ANDRIUSHCHENKO, R.; ARND, H.; JUNGES, S.; KŘETÍNSKÝ, J. Tools at the Frontiers of Quantitative Verification: QComp 2023 Competition Report. In International TOOLympics Challenge. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland AG, 2024.
p. 90-146. ISBN: 978-3-031-67694-9. DetailHELFRICH, M.; ANDRIUSHCHENKO, R.; ČEŠKA, M.; KŘETÍNSKÝ, J.; MARTIČEK, Š.; ŠAFRÁNEK, D. Abstraction-based segmental simulation of reaction networks using adaptive memoization. BMC BIOINFORMATICS, 2024, vol. 25, no. 1,
p. 1-24. ISSN: 1471-2105. Detail -
2023
ANDRIUSHCHENKO, R.; ALEXANDER, B.; ČEŠKA, M.; JUNGES, S.; KATOEN, J.; MACÁK, F. Search and Explore: Symbiotic Policy Synthesis in POMDPs. In Computer Aided Verification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer Verlag, 2023.
p. 113-135. ISBN: 978-3-031-37708-2. DetailANDRIUSHCHENKO, R.; BARTOCCI, E.; ČEŠKA, M.; FRANCESCO, P.; SARAH, S. Deductive Controller Synthesis for Probabilistic Hyperproperties. In Quantitative Evaluation of SysTems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer Verlag, 2023.
p. 288-306. ISBN: 978-3-031-43834-9. Detail -
2022
ANDRIUSHCHENKO, R.; ČEŠKA, M.; JUNGES, S.; KATOEN, J. Inductive Synthesis of Finite-State Controllers for POMDPs. In Conference on Uncertainty in Artificial Intelligence. Proceedings of Machine Learning Research. Eindhoven: Proceedings of Machine Learning Research, 2022.
p. 85-95. ISSN: 2640-3498. DetailANDRIUSHCHENKO, R.; ČEŠKA, M.; MARCIN, V.; VOJNAR, T. GPU-Accelerated Synthesis of Probabilistic Programs. In International Conference on Computer Aided Systems Theory (EUROCAST'22). Lecture Notes in Computer Science. Cham: Springer Nature Switzerland AG, 2022.
p. 256-266. ISBN: 978-3-031-25312-6. Detail -
2021
ANDRIUSHCHENKO, R.; ČEŠKA, M.; ABATE, A.; KWIATKOWSKA, M. Adaptive formal approximations of Markov chains. PERFORMANCE EVALUATION, 2021, vol. 148, no. 102207,
p. 1-23. ISSN: 0166-5316. DetailANDRIUSHCHENKO, R.; ČEŠKA, M.; JUNGES, S.; KATOEN, J. Inductive Synthesis for Probabilistic Programs Reaches New Horizons. International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS). Lecture Notes in Computer Science. Cham: Springer International Publishing, 2021.
p. 191-209. ISBN: 978-3-030-72015-5. DetailANDRIUSHCHENKO, R.; ČEŠKA, M.; STUPINSKÝ, Š.; JUNGES, S.; KATOEN, J. PAYNT: A Tool for Inductive Synthesis of Probabilistic Programs. In International Conference on Computer Aided Verification (CAV). Lecture Notes in Computer Science. Cham: Springer Verlag, 2021.
p. 856-869. ISBN: 978-3-030-81684-1. Detail