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.

翻译：许多信息-物理-人类系统（CPHS）涉及人类决策者，他们可能接收来自人工智能（AI）平台的建议，但最终仍需承担决策责任。在此类CPHS应用中，人类决策者可能因多种原因偏离最优推荐决策，转而执行其他决策。本文提出一个严谨的框架来应对这一挑战。在该框架中，我们考虑人类对系统状态的感知与AI平台存在差异，从而可能偏离AI推荐。我们建立了最优推荐策略的结构特性，并开发了AI所使用的近似人类模型（AHM）。我们给出了因AHM产生的最优性差距的理论边界，并通过数值算例验证了结果的有效性。