Research outputs
2026
Good-for-MDP State Reduction for Stochastic LTL Planning
Weinhuber, C., Giacomo, G. D., Li, Y., Schewe, S., & Tang, Q. (2026). Good-for-MDP State Reduction for Stochastic LTL Planning. In Proceedings of the Aaai Conference on Artificial Intelligence Vol. 40 (pp. 36457-36465). doi:10.1609/aaai.v40i43.40967
The Complexity of Games with Randomised Control
Bahmani, S., Ibsen-Jensen, R., Paul, S., Schewe, S., Slivovsky, F., Tang, Q., . . . Zhu, S. (2026). The Complexity of Games with Randomised Control. In Lecture Notes in Computer Science Vol. 16503 LNCS (pp. 43-64). doi:10.1007/978-3-032-22730-0_3
2025
Efficient Learning of Weak Deterministic Buchi Automata
Alluwaym, M., Li, Y., Schewe, S., & Tang, Q. (2025). Efficient Learning of Weak Deterministic Buchi Automata. In ECAI 2025 Vol. 413 (pp. 1663-1670). doi:10.3233/FAIA250993
Resolving Nondeterminism by Chance
Paul, S., Purser, D., Schewe, S., Tang, Q., Totzke, P., & Yen, D. -D. (2025). Resolving Nondeterminism by Chance. In 36TH INTERNATIONAL CONFERENCE ON CONCURRENCY THEORY, CONCUR 2025 Vol. 348 (pp. 22 pages). doi:10.4230/LIPIcs.CONCUR.2025.32
Accelerating Markov Chain Model Checking: Good-for-Games Meets Unambiguous Automata
Li, Y., Paul, S., Schewe, S., & Tang, Q. (2025). Accelerating Markov Chain Model Checking: Good-for-Games Meets Unambiguous Automata. In COMPUTER AIDED VERIFICATION, CAV 2025, PT II Vol. 15932 (pp. 276-298). doi:10.1007/978-3-031-98679-6_13
2024
Minimising the Probabilistic Bisimilarity Distance
Kiefer, S., & Tang, Q. (2024). Minimising the Probabilistic Bisimilarity Distance. In Leibniz International Proceedings in Informatics Lipics Vol. 311. doi:10.4230/LIPIcs.CONCUR.2024.32
Semantic flowers for good-for-games and deterministic automata
Dell'Erba, D., Schewe, S., Tang, Q., & Zhanabekova, T. (2024). Semantic flowers for good-for-games and deterministic automata. INFORMATION PROCESSING LETTERS, 185, 4 pages. doi:10.1016/j.ipl.2023.106468
Bridging formal methods and machine learning with model checking and global optimisation
Bensalem, S., Huang, X., Ruan, W., Tang, Q., Wu, C., & Zhao, X. (2024). Bridging formal methods and machine learning with model checking and global optimisation. Journal of Logical and Algebraic Methods in Programming, 137, 100941. doi:10.1016/j.jlamp.2023.100941
Angluin-Style Learning of Deterministic Buchi and Co-Buchi Automata
Li, Y., Schewe, S., & Tang, Q. (2024). Angluin-Style Learning of Deterministic Buchi and Co-Buchi Automata. In PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024 (pp. 4506-4514). Retrieved from https://www.webofscience.com/
Angluin-Style Learning of Deterministic Büchi and Co-Büchi Automata
Li, Y., Schewe, S., & Tang, Q. (2024). Angluin-Style Learning of Deterministic Büchi and Co-Büchi Automata. In Ijcai International Joint Conference on Artificial Intelligence (pp. 4506-4514).
2023
Deciding What Is Good-For-MDPs
Schewe, S., Tang, Q., & Zhanabekova, T. (2023). Deciding What Is Good-For-MDPs. In Leibniz International Proceedings in Informatics Lipics Vol. 279. doi:10.4230/LIPIcs.CONCUR.2023.35
A Novel Family of Finite Automata for Recognizing and Learning.-Regular Languages
Li, Y., Schewe, S., & Tang, Q. (2023). A Novel Family of Finite Automata for Recognizing and Learning.-Regular Languages. In Unknown Book (Vol. 14215, pp. 53-73). doi:10.1007/978-3-031-45329-8_3
2022
Strategies for MDP Bisimilarity Equivalence and Inequivalence
Kiefer, S., & Tang, Q. (2022). Strategies for MDP Bisimilarity Equivalence and Inequivalence. In Leibniz International Proceedings in Informatics Lipics Vol. 243. doi:10.4230/LIPIcs.CONCUR.2022.32
Bridging Formal Methods and Machine Learning with Global Optimisation
Huang, X., Ruan, W., Tang, Q., & Zhao, X. (2022). Bridging Formal Methods and Machine Learning with Global Optimisation. In Unknown Book (Vol. 13478, pp. 1-19). doi:10.1007/978-3-031-17244-1_1
2021
Approximate Bisimulation Minimisation
Kiefer, S., & Tang, Q. (2021). Approximate Bisimulation Minimisation. In Leibniz International Proceedings in Informatics Lipics Vol. 213. doi:10.4230/LIPIcs.FSTTCS.2021.48
Computing Probabilistic Bisimilarity Distances for Probabilistic Automata
COMPUTING PROBABILISTIC BISIMILARITY DISTANCES FOR PROBABILISTIC AUTOMATA
Bacci, G., Bacci, G., Larsen, K. G., Mardare, R., Tang, Q., & van Breugel, F. (2021). COMPUTING PROBABILISTIC BISIMILARITY DISTANCES FOR PROBABILISTIC AUTOMATA. LOGICAL METHODS IN COMPUTER SCIENCE, 17(1), 36 pages. doi:10.23638/LMCS-17(1:9)2021
Probabilistic Model Checking of Randomized Java Code
Fatmi, S. Z., Chen, X., Dhamija, Y., Wildes, M., Tang, Q., & van Breugel, F. (2021). Probabilistic Model Checking of Randomized Java Code. In Unknown Book (Vol. 12864, pp. 157-174). doi:10.1007/978-3-030-84629-9_9
2020
Comparing labelled markov decision processes
Kiefer, S., & Tang, Q. (2020). Comparing labelled markov decision processes. In Leibniz International Proceedings in Informatics Lipics Vol. 182. doi:10.4230/LIPIcs.FSTTCS.2020.49
Deciding probabilistic bisimilarity distance one for probabilistic automata
Tang, Q., & van Breugel, F. (2020). Deciding probabilistic bisimilarity distance one for probabilistic automata. JOURNAL OF COMPUTER AND SYSTEM SCIENCES, 111, 57-84. doi:10.1016/j.jcss.2020.02.003
2019
Visual Analytics for Concurrent Java Executions
Artho, C., Pande, M., & Tang, Q. (2019). Visual Analytics for Concurrent Java Executions. In 34TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2019) (pp. 1102-1105). doi:10.1109/ASE.2019.00112
Compiler Fuzzing: How Much Does It Matter?
Marcozzi, M., Tang, Q., Donaldson, A. F., & Cadar, C. (2019). Compiler Fuzzing: How Much Does It Matter?. PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL, 3. doi:10.1145/3360581
A Systematic Impact Study for Fuzzer-Found Compiler Bugs
Computing probabilistic bisimilarity distances for probabilistic automata
Bacci, G., Bacci, G., Larsen, K. G., Mardare, R., Tang, Q., & van Breugel, F. (2019). Computing probabilistic bisimilarity distances for probabilistic automata. In Leibniz International Proceedings in Informatics Lipics Vol. 140. doi:10.4230/LIPIcs.CONCUR.2019.9
2018
Deciding probabilistic bisimilarity distance one for probabilistic automata
Tang, Q., & Van Breugel, F. (2018). Deciding probabilistic bisimilarity distance one for probabilistic automata. In Leibniz International Proceedings in Informatics Lipics Vol. 118. doi:10.4230/LIPIcs.CONCUR.2018.9
Deciding Probabilistic Bisimilarity Distance One for Labelled Markov Chains
Tang, Q., & van Breugel, F. (2018). Deciding Probabilistic Bisimilarity Distance One for Labelled Markov Chains. In Unknown Book (Vol. 10981, pp. 681-699). doi:10.1007/978-3-319-96145-3_39
2017
Algorithms to compute probabilistic bisimilarity distances for labelled Markov chains
Tang, Q., & Breugel, F. V. (2017). Algorithms to compute probabilistic bisimilarity distances for labelled Markov chains. In Leibniz International Proceedings in Informatics Lipics Vol. 85. doi:10.4230/LIPIcs.CONCUR.2017.27
2016
Computing probabilistic bisimilarity distances via policy iteration
Tang, Q., & Van Breugel, F. (2016). Computing probabilistic bisimilarity distances via policy iteration. In Leibniz International Proceedings in Informatics Lipics Vol. 59. doi:10.4230/LIPIcs.CONCUR.2016.22