# Uncertainty Quantification for Competency Assessment of Autonomous Agents

arXiv: https://arxiv.org/abs/2206.10553 · HTML: https://mlsanity.com/paper/2206.10553/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 21 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- ["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](https://mlsanity.com/paper/1711.03846/) (2017)
- [Competency Assessment for Autonomous Agents using Deep Generative Models](https://mlsanity.com/paper/2203.12670/) (2022)

## Built on by (most cited first, 2 of 2)

- [Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories](https://mlsanity.com/paper/2302.08669/) (2023)
- [Preventing Rogue Agents Improves Multi-Agent Collaboration](https://mlsanity.com/paper/2502.05986/) (2025)

## Abstract (arXiv)

For safe and reliable deployment in the real world, autonomous agents must elicit appropriate levels of trust from human users. One method to build trust is to have agents assess and communicate their own competencies for performing given tasks. Competency depends on the uncertainties affecting the agent, making accurate uncertainty quantification vital for competency assessment. In this work, we show how ensembles of deep generative models can be used to quantify the agent's aleatoric and epistemic uncertainties when forecasting task outcomes as part of competency assessment.