تقرير
Uncertainty Quantification for Competency Assessment of Autonomous Agents
| العنوان: | Uncertainty Quantification for Competency Assessment of Autonomous Agents |
|---|---|
| المؤلفون: | Acharya, Aastha, Russell, Rebecca, Ahmed, Nisar R. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | 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. Comment: Accepted at the Workshop on Safe and Reliable Robot Autonomy under Uncertainty at ICRA 2022, Philadelphia, USA |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2206.10553Test |
| رقم الانضمام: | edsarx.2206.10553 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |