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ORCIDKenneth D. Bogert
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2020 – today
- 2022
[c7]Kenneth D. Bogert, Prashant Doshi:
A Hierarchical Bayesian Process for Inverse RL in Partially-Controlled Environments. AAMAS 2022: 145-153- [i2]Kenneth D. Bogert, Yikang Gui, Prashant Doshi:
IRL with Partial Observations using the Principle of Uncertain Maximum Entropy. CoRR abs/2208.06988 (2022) - 2021
- [i1]Kenneth D. Bogert, Prashant Doshi:
A Hierarchical Bayesian model for Inverse RL in Partially-Controlled Environments. CoRR abs/2107.05818 (2021)
2010 – 2019
- 2018
- [j1]Kenneth D. Bogert
, Prashant Doshi:
Multi-robot inverse reinforcement learning under occlusion with estimation of state transitions. Artif. Intell. 263: 46-73 (2018) - 2017
- [c6]Kenneth D. Bogert, Prashant Doshi:
Scaling Expectation-Maximization for Inverse Reinforcement Learning to Multiple Robots under Occlusion. AAMAS 2017: 522-529 - 2016
- [c5]Kenneth D. Bogert, Jonathan Feng-Shun Lin, Prashant Doshi, Dana Kulic:
Expectation-Maximization for Inverse Reinforcement Learning with Hidden Data. AAMAS 2016: 1034-1042 - 2015
- [c4]Kenneth D. Bogert, Prashant Doshi:
Multi-Robot Inverse Reinforcement Learning Under Occlusion with State Transition Estimation. AAMAS 2015: 1837-1838 - [c3]Kenneth D. Bogert, Sina Solaimanpour, Prashant Doshi:
Aerial Robotic Simulations for Evaluation of Multi-Agent Planning in GaTAC. AAMAS 2015: 1919-1920 - [c2]Kenneth D. Bogert, Prashant Doshi:
Toward Estimating Others' Transition Models Under Occlusion for Multi-Robot IRL. IJCAI 2015: 1867-1873 - 2014
- [c1]Kenneth D. Bogert, Prashant Doshi:
Multi-robot inverse reinforcement learning under occlusion with interactions. AAMAS 2014: 173-180
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