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Found 9 papers, 1 papers with code
Using Surprise Index for Competency Assessment in Autonomous Decision-Making
This paper considers the problem of evaluating an autonomous system's competency in performing a task, particularly when working in dynamic and uncertain environments.
Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation
We then test our new non-monolithic CI algorithm on a large-scale target tracking simulation and show that it achieves a tighter bound and a more accurate estimate compared to the original monolithic CI.
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.
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.
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems
A current standard policy for AL is to query the oracle (e. g., the analyst) to refine labels for datapoints where the classifier has the highest uncertainty.
Factorized Machine Self-Confidence for Decision-Making Agents
Markov decision processes underlie much of the theory of reinforcement learning, and are commonly used for planning and decision making under uncertainty in robotics and autonomous systems.
"Dave...I can assure you...that it's going to be all right..." -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships
People who design, use, and are affected by autonomous artificially intelligent agents want to be able to \emph{trust} such agents -- that is, to know that these agents will perform correctly, to understand the reasoning behind their actions, and to know how to use them appropriately.
