# A Factor-Based Framework for Decision-Making Competency Self-Assessment

arXiv: https://arxiv.org/abs/2203.11981 · HTML: https://mlsanity.com/paper/2203.11981/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 2 of 2)

- ["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)
- [Factorized Machine Self-Confidence for Decision-Making Agents](https://mlsanity.com/paper/1810.06519/) (2018)

## Built on by (most cited first, 1 of 1)

- [Using Surprise Index for Competency Assessment in Autonomous Decision-Making](https://mlsanity.com/paper/2312.09033/) (2023)

## Abstract (arXiv)

We summarize our efforts to date in developing a framework for generating succinct human-understandable competency self-assessments in terms of machine self confidence, i.e. a robot's self-trust in its functional abilities to accomplish assigned tasks. Whereas early work explored machine self-confidence in ad hoc ways for niche applications, our Factorized Machine Self-Confidence framework introduces and combines several aspects of probabilistic meta reasoning for algorithmic planning and decision-making under uncertainty to arrive at a novel set of generalizable self-confidence factors, which can support competency assessment for a wide variety of problems.