| Learning linear temporal properties D Neider, I Gavran 2018 Formal Methods in Computer Aided Design (FMCAD), 1-10, 2018 | 31 | 2018 |
| Rely/guarantee reasoning for asynchronous programs I Gavran, F Niksic, A Kanade, R Majumdar, V Vafeiadis 26th International Conference on Concurrency Theory (CONCUR 2015), 2015 | 21 | 2015 |
| Antlab: a multi-robot task server I Gavran, R Majumdar, I Saha ACM Transactions on Embedded Computing Systems (TECS) 16 (5s), 1-19, 2017 | 17 | 2017 |
| Joint inference of reward machines and policies for reinforcement learning Z Xu, I Gavran, Y Ahmad, R Majumdar, D Neider, U Topcu, B Wu Proceedings of the International Conference on Automated Planning and …, 2020 | 11 | 2020 |
| Learning Properties in LTL∩ ACTL from Positive Examples Only R Ehlers, I Gavran, D Neider 2020 Formal Methods in Computer Aided Design (FMCAD), 104-112, 2020 | 1 | 2020 |
| Simultaneous Re-Planning and Plan Execution for Online Job Arrival I Gavran, M Fickert, I Fedotov, J Hoffmann, R Majumdar HSDIP 2019, 55, 2019 | 1 | 2019 |
| Tᴏᴏʟ: accessible automated reasoning for human robot collaboration I Gavran, O Mailahn, R Müller, R Peifer, D Zufferey Proceedings of the 2018 ACM SIGPLAN International Symposium on New Ideas …, 2018 | 1 | 2018 |
| The robot routing problem for collecting aggregate stochastic rewards R Dimitrova, I Gavran, R Majumdar, VS Prabhu, SEZ Soudjani arXiv preprint arXiv:1704.05303, 2017 | 1 | 2017 |
| Choosing the Initial State for Online Replanning M Fickert, I Gavran, I Fedotov, J Hoffmann, R Majumdar, W Ruml | | 2021 |
| Lassie: HOL4 Tactics by Example H Becker, N Bos, I Gavran, E Darulova, R Majumdar arXiv preprint arXiv:2101.00930, 2021 | | 2021 |
| Interactive synthesis of temporal specifications from examples and natural language I Gavran, E Darulova, R Majumdar Proceedings of the ACM on Programming Languages 4 (OOPSLA), 1-26, 2020 | | 2020 |
| Precise but Natural Specification for Robot Tasks I Gavran, B Boldt, E Darulova, R Majumdar arXiv preprint arXiv:1803.02238, 2018 | | 2018 |
| Advice-Guided Reinforcement Learning in a non-Markovian Environment D Neider, JR Gaglione, I Gavran, U Topcu, B Wu, Z Xu | | |
| SAT/SMT S Asadi, M Blicha, A Hyvärinen, G Fedyukovich, N Sharygina, A Cimatti, ... | | |
