| The limits of machine intelligence: Despite progress in machine intelligence, artificial general intelligence is still a major challenge H Shevlin, K Vold, M Crosby, M Halina EMBO reports 20 (10), e49177, 2019 | 102 | 2019 |
| SkiROS—a skill-based robot control platform on top of ROS F Rovida, M Crosby, D Holz, AS Polydoros, B Großmann, RPA Petrick, ... Robot Operating System (ROS) The Complete Reference (Volume 2), 121-160, 2017 | 96 | 2017 |
| Automated agent decomposition for classical planning M Crosby, M Rovatsos, R Petrick Proceedings of the International Conference on Automated Planning and …, 2013 | 80 | 2013 |
| Introduction to the JAGI special issue ‘On defining artificial intelligence’–commentaries and author’s response D Monett, CW Lewis, KR Thórisson, J Bach, G Baldassarre, G Granato, ... Journal of Artificial General Intelligence 11 (2), 1-100, 2020 | 73 | 2020 |
| A single-agent approach to multiagent planning M Crosby, A Jonsson, M Rovatsos ECAI 2014, 237-242, 2014 | 64 | 2014 |
| Mapping intelligence: Requirements and possibilities S Bhatnagar, A Alexandrova, S Avin, S Cave, L Cheke, M Crosby, ... Philosophy and theory of artificial intelligence 2017, 117-135, 2018 | 59 | 2018 |
| The Animal-AI Olympics M Crosby, B Beyret, M Halina Nature Machine Intelligence 1 (5), 257, 2019 | 58 | 2019 |
| A vertical and cyber–physical integration of cognitive robots in manufacturing V Krueger, A Chazoule, M Crosby, A Lasnier, MR Pedersen, F Rovida, ... Proceedings of the IEEE 104 (5), 1114-1127, 2016 | 56 | 2016 |
| Artificial intelligence and the common sense of animals M Shanahan, M Crosby, B Beyret, L Cheke Trends in Cognitive Sciences 24 (11), 862-872, 2020 | 55 | 2020 |
| The societal implications of deep reinforcement learning J Whittlestone, K Arulkumaran, M Crosby Journal of Artificial Intelligence Research 70, 1003–1030-1003–1030, 2021 | 53 | 2021 |
| Testing the vertical and cyber-physical integration of cognitive robots in manufacturing V Krueger, F Rovida, B Grossmann, R Petrick, M Crosby, A Charzoule, ... Robotics and Computer-Integrated Manufacturing 57, 213-219, 2019 | 49 | 2019 |
| The animal-AI testbed and competition M Crosby, B Beyret, M Shanahan, J Hernández-Orallo, L Cheke, M Halina Neurips 2019 competition and demonstration track, 164-176, 2020 | 48 | 2020 |
| The animal-ai environment: Training and testing animal-like artificial cognition B Beyret, J Hernández-Orallo, L Cheke, M Halina, M Shanahan, M Crosby arXiv preprint arXiv:1909.07483, 2019 | 37 | 2019 |
| Building thinking machines by solving animal cognition tasks M Crosby Minds and Machines 30 (4), 589-615, 2020 | 36 | 2020 |
| Integrating Mission and Task Planning in an Industrial Robotics Framework M Crosby, F Rovida, V Krüger, RPA Petrick Proceedings of the 27th International Conference on Automated Planning and …, 2017 | 34 | 2017 |
| Temporal multiagent planning with concurrent action constraints M Crosby, RPA Petrick Proc 2nd ICAPS Workshop on Distributed and Multi-Agent Planning. ICAPS, 16-24, 2014 | 28 | 2014 |
| Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks AM Proca, FE Rosas, AI Luppi, D Bor, M Crosby, PAM Mediano PLOS Computational Biology 20 (6), e1012178, 2024 | 23 | 2024 |
| Detect, understand, act: A neuro-symbolic hierarchical reinforcement learning framework L Mitchener, D Tuckey, M Crosby, A Russo Machine Learning 111 (4), 1523-1549, 2022 | 21 | 2022 |
| Heuristic multiagent planning with self-interested agents M Crosby, M Rovatsos The 10th International Conference on Autonomous Agents and Multiagent …, 2011 | 17 | 2011 |
| Ó hÉigeartaigh S Bhatnagar, A Alexandrova, S Avin, S Cave, L Cheke, M Crosby, ... S., Martínez-Plumed, F., Price, H., Shevlin, H., Weller, A., Winfield, A …, 2018 | 14 | 2018 |