A Q-learning Approach for Adherence-Aware Recommendations
Machine Learning
2024-07-18 v1 Systems and Control
Systems and Control
Abstract
In many real-world scenarios involving high-stakes and safety implications, a human decision-maker (HDM) may receive recommendations from an artificial intelligence while holding the ultimate responsibility of making decisions. In this letter, we develop an "adherence-aware Q-learning" algorithm to address this problem. The algorithm learns the "adherence level" that captures the frequency with which an HDM follows the recommended actions and derives the best recommendation policy in real time. We prove the convergence of the proposed Q-learning algorithm to the optimal value and evaluate its performance across various scenarios.
Cite
@article{arxiv.2309.06519,
title = {A Q-learning Approach for Adherence-Aware Recommendations},
author = {Ioannis Faros and Aditya Dave and Andreas A. Malikopoulos},
journal= {arXiv preprint arXiv:2309.06519},
year = {2024}
}