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

Many cyber-physical-human systems (CPHSs) involve a human decision-maker who acts using recommendations from an artificial intelligence (AI) platform. In such CPHS applications, the human decision-maker may depart from an optimal recommended decision and instead implement a different one for various reasons, resulting in a loss in performance. In this letter, we develop a rigorous framework to overcome this challenge. In our framework, humans may deviate from AI recommendations as they interpret the system's state differently to 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.