Humans working with autonomous artificially intelligent systems may not be experts in the inner workings of their machine teammates, but need to understand when to employ, trust, and rely on the system. A critical challenge is to develop machine agents with the capacity to understand their own capabilities and limitations, and the ability to communicate this information to human partners. Self-assessment is an emerging field that tackles this challenge through the development of algorithms that enable autonomous agents to understand and communicate their competency. These methods can engender appropriate trust and align human expectations with autonomous assistant abilities. However, current research in self-assessment is dispersed across many fields, including artificial intelligence, robotics, and human factors. This survey connects work from these disparate areas and reviews state-of-the-art methods for algorithmic self-assessments that enable autonomous agents to estimate, understand, and communicate valuable information pertaining to their competency, with focus on methods that can improve interactions within human-machine teams. To better understand the landscape of self-assessment approaches, we present a framework for categorizing work in self-assessment based on underlying algorithm type: test-based, learning-based, or knowledge-based. We synthesize common features across these approaches and discuss relevant future directions for research in this emerging space.

使用自主人工智能系统的人类可能不是机器队友内部运作的专家，但需要了解何时使用、信任和依赖该系统。一个关键的挑战是开发能够了解自身能力和局限性的机器代理，并能够将这些信息传达给人类合作伙伴。自我评估是一个新兴领域，它通过开发算法来应对这一挑战，使自主代理能够理解和传达他们的能力。这些方法可以产生适当的信任，并使人类期望与自主助理能力保持一致。然而，目前自我评估的研究分散在许多领域，包括人工智能、机器人和人为因素。这项调查将这些不同领域的工作联系起来，并回顾了最先进的算法自我评估方法，使自主代理能够估计、理解和交流与其能力相关的有价值的信息，重点关注可以改善内部交互的方法。人机团队。为了更好地了解自我评估方法的前景，我们提出了一个框架，用于根据基础算法类型对自我评估工作进行分类：基于测试、基于学习或基于知识。我们综合了这些方法的共同特征，并讨论了这一新兴领域的相关未来研究方向。