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

arXiv: https://arxiv.org/abs/2403.05715 · HTML: https://mlsanity.com/paper/2403.05715/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Approximate information state for approximate planning and reinforcement learning in partially observed systems](https://mlsanity.com/paper/2010.08843/) (2020)
- [The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations](https://mlsanity.com/paper/2209.01874/) (2022)
- [Learning to Make Adherence-Aware Advice](https://mlsanity.com/paper/2310.00817/) (2023)
- [On Team Decision Problems with Nonclassical Information Structures](https://mlsanity.com/paper/2101.10992/) (2021)
- [Separation of Learning and Control for Cyber-Physical Systems](https://mlsanity.com/paper/2107.06379/) (2021)
- [Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging](https://mlsanity.com/paper/2304.00397/) (2023)
- [Social Media and Misleading Information in a Democracy: A Mechanism Design Approach](https://mlsanity.com/paper/2003.07192/) (2020)
- [A Q-learning Approach for Adherence-Aware Recommendations](https://mlsanity.com/paper/2309.06519/) (2023)
- [Worst-Case Control and Learning Using Partial Observations Over an Infinite Time-Horizon](https://mlsanity.com/paper/2303.16321/) (2023)
- [Human Behavioral Models Using Utility Theory and Prospect Theory](https://mlsanity.com/paper/2210.07322/) (2022)
- [Routing in Mixed Transportation Systems for Mobility Equity](https://mlsanity.com/paper/2309.03981/) (2023)

## Built on by (most cited first, 4 of 4)

- [AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning](https://mlsanity.com/paper/2504.20187/) (2025)
- [Off-Policy Evaluation for Sequential Persuasion Process with Unobserved Confounding](https://mlsanity.com/paper/2504.01211/) (2025)
- [Model-Based Reinforcement Learning Under Confounding](https://mlsanity.com/paper/2512.07528/) (2025)
- [Cross-fitted Proximal Learning for Model-Based Reinforcement Learning](https://mlsanity.com/paper/2604.05185/) (2026)

## Abstract (arXiv)

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.