Human-Readable Neuro-Fuzzy Networks from Frequent Yet Discernible Patterns in Reward-Based Environments
Human-Readable Neuro-Fuzzy Networks from Frequent Yet Discernible Patterns in Reward-Based Environments
John Wesley Hostetter, Adittya Soukarjya Saha, Md Mirajul Islam, Tiffany Barnes, Min Chi
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Main Track. Pages 4535-4543.
https://doi.org/10.24963/ijcai.2025/505
We propose self-organizing and simplifying neuro-fuzzy networks (NFNs) to yield transparent human-readable policies by exploiting fuzzy information granulation and graph theory. Deriving from social network analysis, we retain only the frequent-yet-discernible (FYD) patterns in NFNs and apply them to reward-based scenarios. The effectiveness of NFNs from FYD patterns is shown in classic control and a real-world classroom using an intelligent tutoring system to teach students.
Keywords:
Knowledge Representation and Reasoning: KRR: Learning and reasoning
Data Mining: DM: Frequent pattern mining
Humans and AI: HAI: Computer-aided education
Machine Learning: ML: Neuro-symbolic methods/Abductive Learning