---
title: "Human-Readable Neuro-Fuzzy Networks from Frequent Yet Discernible Patterns in Reward-Based Environments"
authors:
  - "John Wesley Hostetter"
  - "Adittya Soukarjya Saha"
  - "Md Mirajul Islam"
  - "Tiffany Barnes"
  - "Min Chi"
doi: "10.24963/ijcai.2025/505"
url: "https://ijcai.org/proceedings/2025/505"
pdf: "https://ijcai.org/proceedings/2025/0505.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "4535-4543"
track: "Main Track"
keywords:
  - "Humans and AI: HAI: Computer-aided education"
  - "Data Mining: DM: Frequent pattern mining"
  - "Knowledge Representation and Reasoning: KRR: Learning and reasoning"
  - "Machine Learning: ML: Neuro-symbolic methods/Abductive Learning"
---

# 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*, pages 4535-4543. DOI: [10.24963/ijcai.2025/505](https://doi.org/10.24963/ijcai.2025/505)

## Abstract

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.