mlsanityData to 7 Oct 2026

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

arXiv 2403.05715 ↗First posted 8 Mar 2024eess.SY cs.AI cs.HCcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 8 Mar 2024.

Builds on

12 of 25 references.

References · 12

  1. 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 345
  2. 2020Approximate information state for approximate planning and reinforcement learning in partially observed systemscited by 67
  3. 2022The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendationscited by 16
  4. 2023Learning to Make Adherence-Aware Advicecited by 13
  5. 2021On Team Decision Problems with Nonclassical Information Structurescited by 10
  6. 2021Separation of Learning and Control for Cyber-Physical Systemscited by 8
  7. 2023Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Mergingcited by 7
  8. 2020Social Media and Misleading Information in a Democracy: A Mechanism Design Approachcited by 5
  9. 2023A Q-learning Approach for Adherence-Aware Recommendationscited by 4
  10. 2023Worst-Case Control and Learning Using Partial Observations Over an Infinite Time-Horizoncited by 3
  11. 2022Human Behavioral Models Using Utility Theory and Prospect Theorycited by 1
  12. 2023Routing in Mixed Transportation Systems for Mobility Equitycited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learningcited by 3
  2. 2025Off-Policy Evaluation for Sequential Persuasion Process with Unobserved Confoundingcited by 1
  3. 2025Model-Based Reinforcement Learning Under Confoundingcited by 1
  4. 2026Cross-fitted Proximal Learning for Model-Based Reinforcement Learningcited by 0