论文标题
提高对可靠自主权的能力
Improving Competence for Reliable Autonomy
论文作者
论文摘要
考虑到现实世界中非结构化域的复杂性，对于包括处理自主系统可能遇到的所有可能场景所需的所有功能的设计模型通常是不可能或不切实际的。为了使自治系统在此类域中可靠，它应该具有在线提高其能力的能力。在本文中，我们提出了一种在部署过程中提高系统能力的方法。我们特别关注一类的半自治系统，称为能力感知的系统，这些系统是对自己的能力建模的 - 在任何给定情况下使用的自主权的最佳范围 - 并通过与人类权威的互动获得的反馈来了解这种能力。我们的方法利用此类反馈来确定系统初始模型中缺少重要的状态特征，并将其纳入其状态表示。结果是一种更好地预测人类参与的代理，从而提高了其能力和可靠性，从而提高了其整体绩效。
Given the complexity of real-world, unstructured domains, it is often impossible or impractical to design models that include every feature needed to handle all possible scenarios that an autonomous system may encounter. For an autonomous system to be reliable in such domains, it should have the ability to improve its competence online. In this paper, we propose a method for improving the competence of a system over the course of its deployment. We specifically focus on a class of semi-autonomous systems known as competence-aware systems that model their own competence -- the optimal extent of autonomy to use in any given situation -- and learn this competence over time from feedback received through interactions with a human authority. Our method exploits such feedback to identify important state features missing from the system's initial model, and incorporates them into its state representation. The result is an agent that better predicts human involvement, leading to improvements in its competence and reliability, and as a result, its overall performance.
