Uncertainty Quantification for Competency Assessment of Autonomous Agents
This addresses the need for building trust in autonomous agents for real-world applications, though it appears incremental as it applies existing ensemble methods to a specific competency assessment context.
The paper tackles the problem of enabling autonomous agents to assess and communicate their own competencies for safe deployment by using ensembles of deep generative models to quantify aleatoric and epistemic uncertainties in task outcome forecasting.
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.