The Causal-Entropic Fuzzy Inference: A Bayesian Framework for Explainable and Robust Reasoning
DOI:
https://doi.org/10.59247/jfsc.v4i1.392Keywords:
Fuzzy Reasoning, Causal Discovery, Bayesian Inference, Free Energy Principle, Explainable AI, Reductive PropertyAbstract
Traditional fuzzy reasoning methods exhibit limitations in satisfying the reductive property and handling uncertain environments. This paper proposes a novel Causal-Entropic Fuzzy Inference (CEFI) framework that integrates causal discovery with Bayesian inference to overcome these limitations. The proposed method consists of three main components: (1) a causal rule discovery mechanism based on conditional independence tests, (2) an entropic inference engine utilizing variational free energy minimization, and (3) an active perception module for strategic information gathering. Experimental results on SISO and MISO systems demonstrate that CEFI achieves 99.4% reductive property, outperforming state-of-the-art methods by 7.3-31.2% in noisy environments while providing causal explanations for reasoning processes.
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Copyright (c) 2026 Jin-Hyok Choe, Yon-Ju Jang , Son-Il Kwak , Ok-Sim Ri ; Hyon-U Kong
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