| Expectation-maximization for inverse reinforcement learning with hidden data K Bogert, JFS Lin, P Doshi, D Kulic Proceedings of the 2016 International Conference on Autonomous Agents …, 2016 | 41 | 2016 |
| Multi-robot inverse reinforcement learning under occlusion with interactions K Bogert, P Doshi Proceedings of the 2014 international conference on Autonomous agents and …, 2014 | 41 | 2014 |
| Advancing virtual patient simulations through design research and interPLAY: part I: design and development A Hirumi, A Kleinsmith, K Johnsen, S Kubovec, M Eakins, K Bogert, ... Educational Technology Research and Development 64, 763-785, 2016 | 32 | 2016 |
| Multi-robot inverse reinforcement learning under occlusion with estimation of state transitions K Bogert, P Doshi Artificial Intelligence 263, 46-73, 2018 | 20 | 2018 |
| Toward Estimating Others’ Transition Models Under Occlusion for Multi-Robot IRL K Bogert, P Doshi IJCAI, 1867-1873, 2015 | 17 | 2015 |
| Advancing virtual patient simulations through design research and interPLAY: part II—integration and field test A Hirumi, T Johnson, RJ Reyes, B Lok, K Johnsen, DJ Rivera-Gutierrez, ... Educational technology research and development 64, 1301-1335, 2016 | 14 | 2016 |
| Development and Use of an Interactive Computerized Dog Model to Evaluate Cranial Nerve Knowledge in Veterinary Students K Bogert, S Platt, A Haley, M Kent, G Edwards, H Dookwah, K Johnsen Journal of veterinary medical education 43 (1), 26-32, 2016 | 12 | 2016 |
| Scaling expectation-maximization for inverse reinforcement learning to multiple robots under occlusion K Bogert, P Doshi Proceedings of the 16th Conference on Autonomous Agents and MultiAgent …, 2017 | 11 | 2017 |
| (Abstract) Multi-Robot Inverse Reinforcement Learning Under Occlusion with State Transition Estimation K Bogert, P Doshi AAMAS, 2015 | 10* | 2015 |
| A hierarchical bayesian process for inverse rl in partially-controlled environments K Bogert, P Doshi AAMAS Conference proceedings, 2022 | 5 | 2022 |
| Gaining perspective with google earth virtual reality in an introduction-level physical geology course BD McNamee, K Bogert Proceedings of the GSA Annual Meeting, Indianapolis, IN, USA, 4-7, 2018 | 5 | 2018 |
| Advancing virtual patient simulations and experiential learning with InterPLAY: examining how theory informs design and design informs theory A Hirumi, K Johnson, A Kleinsmith, R Reyes, D Rivera-Gutierrez, ... J Appl Instr Des 6 (1), 49-65, 2017 | 5 | 2017 |
| Poster: Evolution and usability of ubiquitous immersive 3D interfaces A Basu, K Johnsen, K Bogert 2013 IEEE Symposium on 3D User Interfaces (3DUI), 131-132, 2013 | 3 | 2013 |
| Aerial Robotic Simulations for Evaluation of Multi-Agent Planning in GaTAC. KD Bogert, S Solaimanpour, P Doshi AAMAS, 1919-1920, 2015 | 2 | 2015 |
| Immersive virtual reality on-the-go A Basu, K Johnsen, K Bogert, P Wins Virtual Reality (VR), 2013 IEEE, 193-194, 2013 | 2 | 2013 |
| X-lab: XML-based laboratory exercises for CS1 R Bruce, JD Brock, K Bogert Proceedings of the 42nd annual Southeast regional conference, 434-435, 2004 | 2 | 2004 |
| IRL with Partial Observations using the Principle of Uncertain Maximum Entropy K Bogert, Y Gui, P Doshi arXiv preprint arXiv:2208.06988, 2022 | 1 | 2022 |
| Inverse reinforcement learning for robotic applications: hidden variables, multiple experts and unknown dynamics KD Bogert University of Georgia, 2016 | 1 | 2016 |
| The Principle of Uncertain Maximum Entropy K Bogert, M Kothe arXiv preprint arXiv:2305.09868, 2023 | | 2023 |
| Notes on Generalizing the Maximum Entropy Principle to Uncertain Data K Bogert arXiv preprint arXiv:2109.04530, 2021 | | 2021 |
Prashant DoshiProfessor of Computer Science, University of GeorgiaGeverifieerd e-mailadres voor cs.uga.edu
