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Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories
Giving autonomous agents the ability to forecast their own outcomes and uncertainty will allow them to communicate their competencies and be used more safely.
AAAI 2022 Fall Symposium: Lessons Learned for Autonomous Assessment of Machine Abilities (LLAAMA)
Modern civilian and military systems have created a demand for sophisticated intelligent autonomous machines capable of operating in uncertain dynamic environments.
Uncertainty Quantification for Competency Assessment of Autonomous Agents
For safe and reliable deployment in the real world, autonomous agents must elicit appropriate levels of trust from human users.
Competency Assessment for Autonomous Agents using Deep Generative Models
For autonomous agents to act as trustworthy partners to human users, they must be able to reliably communicate their competency for the tasks they are asked to perform.
Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data
The deployment of reinforcement learning (RL) in the real world comes with challenges in calibrating user trust and expectations.
