@article{hostetter_chi_2023, title={Latent Space Encoding for Interpretable Fuzzy Logic Rules in Continuous and Noisy High-Dimensional Spaces}, ISSN={["1544-5615"]}, DOI={10.1109/FUZZ52849.2023.10309706}, abstractNote={This study introduces a general approach for generating fuzzy logic rules in regression tasks with complex, high-dimensional input spaces. The method leverages the power of encoding data into a latent space, where its uniqueness is analyzed to determine whether it merits the distinction of becoming a noteworthy exemplar. The efficacy of the proposed method is showcased through its application in predicting the acceleration of one of the links for the Unimation Puma 560 robot arm, effectively overcoming the challenges posed by non-linearity and noise in the dataset.}, journal={2023 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, FUZZ}, author={Hostetter, John Wesley and Chi, Min}, year={2023} }
 @misc{hostetter_abdelshiheed_barnes_chi_2023, title={Leveraging Fuzzy Logic Towards More Explainable Reinforcement Learning-Induced Pedagogical Policies on Intelligent Tutoring Systems}, ISSN={["1544-5615"]}, url={http://dx.doi.org/10.1109/FUZZ52849.2023.10309741}, DOI={10.1109/FUZZ52849.2023.10309741}, abstractNote={Deep Reinforcement Learning (Deep RL) has revolutionized the field of Intelligent Tutoring Systems by providing effective pedagogical policies. However, the “black box” nature of Deep RL models makes it challenging to understand these policies. This study tackles this challenge by applying fuzzy logic to distill knowledge from Deep RL-induced policies into interpretable IF-THEN Fuzzy Logic Controller (FLC) rules. Our experiments show that these FLC policies significantly outperform expert policy and student decisions, demonstrating the effectiveness of our approach. We propose a Temporal Granule Pattern (TGP) mining algorithm to increase the FLC rules' interpretability further. This work highlights the potential of fuzzy logic and TGP analysis to enhance understanding of Deep RL-induced pedagogical policies.}, journal={2023 IEEE International Conference on Fuzzy Systems (FUZZ)}, publisher={IEEE}, author={Hostetter, John Wesley and Abdelshiheed, Mark and Barnes, Tiffany and Chi, Min}, year={2023}, month={Aug} }
 @inproceedings{hostetter_conati_yang_abdelshiheed_barnes_chi_2023, place={Germany}, title={XAI to Increase the Effectiveness of an Intelligent Pedagogical Agent}, DOI={10.1145/3570945.3607301}, abstractNote={We explore eXplainable AI (XAI) to enhance user experience and understand the value of explanations in AI-driven pedagogical decisions within an Intelligent Pedagogical Agent (IPA). Our real-time and personalized explanations cater to students' attitudes to promote learning. In our empirical study, we evaluate the effectiveness of personalized explanations by comparing three versions of the IPA: (1) personalized explanations and suggestions, (2) suggestions but no explanations, and (3) no suggestions. Our results show the IPA with personalized explanations significantly improves students' learning outcomes compared to the other versions.}, booktitle={Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents. (IVA’23)}, author={Hostetter, J.W. and Conati, C. and Yang, X. and Abdelshiheed, M. and Barnes, T. and Chi, M.}, year={2023} }