论文标题
自治药物能力评估的不确定性量化
Uncertainty Quantification for Competency Assessment of Autonomous Agents
论文作者
论文摘要
为了在现实世界中安全可靠的部署，自主代理必须从人类用户那里获得适当的信任水平。建立信任的一种方法是让代理评估并传达自己执行给定任务的能力。能力取决于影响代理的不确定性，使得准确的不确定性量化对于能力评估至关重要。在这项工作中，我们展示了如何在预测任务结果作为能力评估的一部分时，如何使用深层生成模型的集合来量化代理商的态度和认知不确定性。
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.
