标题： 一种在网络物理人类系统中有效的人工智能推荐框架 显示英文标题

标题： A Framework for Effective AI Recommendations in Cyber-Physical-Human Systems

摘要： 许多网络物理人类系统（CPHS）涉及一个人类决策者，该决策者可能在持有最终决策责任的同时接收来自人工智能（AI）平台的建议。 在这样的CPHS应用中，由于各种原因，人类决策者可能会偏离最优推荐决策，而是实施不同的决策。 在本文中，我们开发了一个严格的框架来克服这一挑战。 在我们的框架中，我们认为人类可能会偏离AI的建议，因为他们以不同于AI平台的方式感知和解释系统的状态。 我们建立了最优推荐策略的结构特性，并开发了一个由AI使用的近似人类模型（AHM）。 我们提供了由于AHM而导致的最优性差距的理论界限，并在一个数值示例中展示了我们结果的有效性。 显示英文摘要

摘要： Many cyber-physical-human systems (CPHS) involve a human decision-maker who may receive recommendations from an artificial intelligence (AI) platform while holding the ultimate responsibility of making decisions. In such CPHS applications, the human decision-maker may depart from an optimal recommended decision and instead implement a different one for various reasons. In this letter, we develop a rigorous framework to overcome this challenge. In our framework, we consider that humans may deviate from AI recommendations as they perceive and interpret the system's state in a different way than the AI platform. We establish the structural properties of optimal recommendation strategies and develop an approximate human model (AHM) used by the AI. We provide theoretical bounds on the optimality gap that arises from an AHM and illustrate the efficacy of our results in a numerical example.