标题： 在人类-人工智能协作中考虑人类对人工智能意图的信念的效用 显示英文标题

标题： On the Utility of Accounting for Human Beliefs about AI Intention in Human-AI Collaboration

摘要： 为了实现有效的人机协作，仅仅优化AI性能而不考虑人类因素是不够的。 最近的研究表明，设计能够考虑人类行为的AI代理可以提高人机协作的性能。 然而，大多数现有方法的一个局限性是它们假设人类行为保持不变，无论AI代理的行为如何。 实际上，人类可能会根据他们对AI意图的信念调整他们的行动，具体来说，就是他们根据AI的行为所认为AI试图完成的子任务。 在本文中，我们通过使协作AI代理考虑其人类伙伴对其意图的信念，即人类伙伴认为AI代理试图完成什么，从而解决这一局限性，并据此设计其行动方案，以促进更有效的人机协作。 具体来说，我们开发了一个人类信念模型，该模型捕捉了人类如何解释和推理他们的AI伙伴的意图。 利用这个信念模型，我们创建了一个AI代理，在制定与人类互动的策略时，同时考虑人类行为和人类信念。 通过大量现实世界的人类受试者实验，我们证明我们的信念模型更准确地捕捉了人类对AI意图的感知。 此外，我们展示了我们的AI代理，其设计考虑到人类对其意图的信念，在人机协作中显著提高了性能。 显示英文摘要

摘要： To enable effective human-AI collaboration, merely optimizing AI performance without considering human factors is insufficient. Recent research has shown that designing AI agents that take human behavior into account leads to improved performance in human-AI collaboration. However, a limitation of most existing approaches is their assumption that human behavior remains static, regardless of the AI agent's actions. In reality, humans may adjust their actions based on their beliefs about the AI's intentions, specifically, the subtasks they perceive the AI to be attempting to complete based on its behavior. In this paper, we address this limitation by enabling a collaborative AI agent to consider its human partner's beliefs about its intentions, i.e., what the human partner thinks the AI agent is trying to accomplish, and to design its action plan accordingly to facilitate more effective human-AI collaboration. Specifically, we developed a model of human beliefs that captures how humans interpret and reason about their AI partner's intentions. Using this belief model, we created an AI agent that incorporates both human behavior and human beliefs when devising its strategy for interacting with humans. Through extensive real-world human-subject experiments, we demonstrate that our belief model more accurately captures human perceptions of AI intentions. Furthermore, we show that our AI agent, designed to account for human beliefs over its intentions, significantly enhances performance in human-AI collaboration.