Learning the parameters of Partially Observable Markov Decision Processes (POMDPs) from limited data is a significant challenge. We introduce the Fuzzy-MAP EM algorithm, a novel approach that incorporates expert knowledge into the parameter estimation process by enriching the Expectation–Maximization (EM) framework with fuzzy pseudo-counts derived from an expert-defined fuzzy model. This integration naturally reformulates the problem as a Maximum A Posteriori (MAP) estimation, effectively guiding learning in environments with limited data. In synthetic medical simulations, our method consistently outperforms the standard EM algorithm under both low-data and high-noise conditions. Furthermore, a case study on Myasthenia Gravis illustrates the ability of the Fuzzy-MAP EM algorithm to recover a clinically coherent POMDP, demonstrating its potential as a practical tool for data-efficient modeling in healthcare.
Locatelli, M., Hommersom, A., Cerioli, R., Besozzi, D., Stella, F. (2026). Expert-Guided POMDP Learning for Data-Efficient Modeling in Healthcare. In Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning First International Joint Conference, HC@AIxIA+HYDRA 2025, Bologna, Italy, October 25–26, 2025, Proceedings (pp.369-383). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-032-16708-8_30].
Expert-Guided POMDP Learning for Data-Efficient Modeling in Healthcare
Locatelli M.;Cerioli R. C.;Besozzi D.;Stella F.
2026
Abstract
Learning the parameters of Partially Observable Markov Decision Processes (POMDPs) from limited data is a significant challenge. We introduce the Fuzzy-MAP EM algorithm, a novel approach that incorporates expert knowledge into the parameter estimation process by enriching the Expectation–Maximization (EM) framework with fuzzy pseudo-counts derived from an expert-defined fuzzy model. This integration naturally reformulates the problem as a Maximum A Posteriori (MAP) estimation, effectively guiding learning in environments with limited data. In synthetic medical simulations, our method consistently outperforms the standard EM algorithm under both low-data and high-noise conditions. Furthermore, a case study on Myasthenia Gravis illustrates the ability of the Fuzzy-MAP EM algorithm to recover a clinically coherent POMDP, demonstrating its potential as a practical tool for data-efficient modeling in healthcare.
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