Articles with public access mandates - Tathagata ChakrabortiLearn more
Available somewhere: 30
Plan explicability and predictability for robot task planning
Y Zhang, S Sreedharan, A Kulkarni, T Chakraborti, HH Zhuo, ...
2017 IEEE international conference on robotics and automation (ICRA), 1313-1320, 2017
Mandates: US Department of Defense, US National Aeronautics and Space Administration …
Explicability? legibility? predictability? transparency? privacy? security? the emerging landscape of interpretable agent behavior
T Chakraborti, A Kulkarni, S Sreedharan, DE Smith, S Kambhampati
Proceedings of the international conference on automated planning and …, 2019
Mandates: US Department of Defense, US National Aeronautics and Space Administration
Plan Explanations as Model Reconciliation -- An Empirical Study
T Chakraborti, S Sreedharan, S Grover, S Kambhampati
2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI …, 2019
Mandates: US Department of Defense, US National Aeronautics and Space Administration
Projection-aware task planning and execution for human-in-the-loop operation of robots in a mixed-reality workspace
T Chakraborti, S Sreedharan, A Kulkarni, S Kambhampati
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018
Mandates: US Department of Defense, US National Aeronautics and Space Administration
MTDeep: boosting the security of deep neural nets against adversarial attacks with moving target defense
S Sengupta, T Chakraborti, S Kambhampati
Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence, 2018
Mandates: US Department of Defense, US National Aeronautics and Space Administration
Handling model uncertainty and multiplicity in explanations via model reconciliation
S Sreedharan, S Kambhampati
Twenty-Eighth International Conference on Automated Planning and Scheduling, 2018
Mandates: US Department of Defense, US National Aeronautics and Space Administration
Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata.
DN Bhattacharyya, Saugat and Sengupta, Abhronil and Chakraborti, Tathagatha ...
Medical & biological engineering \& computing 52, 131--139, 2014
Mandates: Council of Scientific and Industrial Research, India
Virtual, augmented, and mixed reality for human-robot interaction: A survey and virtual design element taxonomy
M Walker, T Phung, T Chakraborti, T Williams, D Szafir
ACM Transactions on Human-Robot Interaction 12 (4), 1-39, 2023
Mandates: US National Science Foundation
RADAR -- A Proactive Decision Support System for Human-in-the-Loop Planning
S Sengupta, T Chakraborti, S Sreedharan, SG Vadlamudi, ...
2017 AAAI Fall Symposium Series, 2017
Mandates: US Department of Defense, US National Aeronautics and Space Administration
Foundations of explanations as model reconciliation
S Sreedharan, T Chakraborti, S Kambhampati
Artificial Intelligence 301, 103558, 2021
Mandates: US National Science Foundation, US Department of Defense, US National …
Explicable Planning as Minimizing Distance from Expected Behavior
A Kulkarni, Y Zha, T Chakraborti, SG Vadlamudi, Y Zhang, ...
Proceedings of the 18th International Conference on Autonomous Agents and …, 2019
Mandates: US National Science Foundation, US Department of Defense, US National …
–d3wa+–a case study of xaip in a model acquisition task for dialogue planning
S Sreedharan, T Chakraborti, C Muise, Y Khazaeni, S Kambhampati
Proceedings of the International Conference on Automated Planning and …, 2020
Mandates: US National Science Foundation, US Department of Defense, US National …
Balancing explicability and explanation in human-aware planning
S Sreedharan, S Kambhampati
2017 AAAI Fall Symposium Series, 2017
Mandates: US Department of Defense, US National Aeronautics and Space Administration
RADAR: automated task planning for proactive decision support
S Grover, S Sengupta, T Chakraborti, AP Mishra, S Kambhampati
Human–Computer Interaction 35 (5-6), 387-412, 2020
Mandates: US National Science Foundation, US Department of Defense, US National …
Towards understanding user preferences for explanation types in model reconciliation
Z Zahedi, A Olmo, T Chakraborti, S Sreedharan, S Kambhampati
2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI …, 2019
Mandates: US Department of Defense, US National Aeronautics and Space Administration
(When) Can AI Bots Lie?
T Chakraborti, S Kambhampati
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 53-59, 2019
Mandates: US Department of Defense, US National Aeronautics and Space Administration
Expectation-aware planning: A unifying framework for synthesizing and executing self-explaining plans for human-aware planning
S Sreedharan, T Chakraborti, C Muise, S Kambhampati
Proceedings of the AAAI Conference on Artificial Intelligence 34 (03), 2518-2526, 2020
Mandates: US Department of Defense, US National Aeronautics and Space Administration
Designing environments conducive to interpretable robot behavior
A Kulkarni, S Sreedharan, S Keren, T Chakraborti, DE Smith, ...
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020
Mandates: US Department of Defense, US National Aeronautics and Space Administration
Human-aware planning revisited: A tale of three models
T Chakraborti, S Sreedharan, S Kambhampati
IJCAI-ECAI XAI/ICAPS XAIP Workshops 10, 2018
Mandates: US Department of Defense, US National Aeronautics and Space Administration
Explicability versus Explanations in Human-Aware Planning
T Chakraborti, S Sreedharan, S Kambhampati
Proceedings of the 17th International Conference on Autonomous Agents and …, 2018
Mandates: US Department of Defense, US National Aeronautics and Space Administration
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