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Finite-State Fuzzy Constrained Markov Decision Processes for Individualized Decision Making
IEEE Transactions on Cybernetics
|August 13, 2026
Summary
This study introduces a fuzzy Constrained Markov Decision Process (CMDP) framework for safer, individualized decision-making. It enables graded safety constraints, overcoming limitations of traditional binary CMDPs, especially in personalized healthcare.
Area of Science:
- Artificial Intelligence
- Control Theory
- Decision Science
Background:
- Constrained Markov Decision Processes (CMDPs) are limited by binary states and strict safety criteria.
- Existing CMDPs struggle with uncertain states and continuous safety needs, hindering applications like personalized healthcare.
- Intersubject variability in healthcare requires more flexible decision-making frameworks.
Purpose of the Study:
- To develop a fuzzy CMDP framework integrating fuzzy states and per-decision safety constraints.
- To address computational intractability by projecting fuzzy states onto a tractable binary CMDP.
- To enable graded, individualized, and state-dependent safety specifications for sequential decision-making.
Main Methods:
- Developed a fuzzy CMDP framework using stochastic fuzzy discrete event systems (SFDESs) theory.
- Introduced a projection method to map safe fuzzy states to binary states for tractability.
- Applied Bellman recursion and dynamic programming on the reduced binary CMDP.
Main Results:
- The proposed projection preserves stochastic semantics and CMDP structure.
- Optimal policies in the reduced CMDP satisfy the fuzzy state safety constraints.
- The framework supports graded, individualized, and state-dependent safety specifications.
Conclusions:
- The fuzzy CMDP framework offers a mathematically rigorous and tractable approach for individual-specific sequential decision-making.
- It enhances decision-making in domains with intersubject variability, such as personalized healthcare.
- The framework unifies fuzzy state evolution with constrained stochastic optimization.
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