自律エージェントの能力評価のための不確かさ定量化
Uncertainty Quantification for Competency Assessment of Autonomous Agents
深層生成モデルのアンサンブルを用いて、自律エージェントがタスク結果を予測する際の偶然的不確かさと認識的不確かさを定量化し、能力評価に役立てる手法を提案した。
著者: Aastha Acharya, Rebecca Russell, Nisar R. Ahmed
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.
