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.

在涉及人工智能平台向人类决策者提供建议但最终决策责任由人类承担的网络物理人系统中，人类决策者可能因各种原因偏离最优建议决策，本文提出了一个严格的框架来解决这个问题，并建立了最优建议策略的结构属性和人工智能使用的近似人类模型，提供了一个数值实例以说明结果的有效性。

智能物理人系统中有效的人工智能推荐框架