Information Entropy-Driven Adaptive Bayesian Model for Autonomous Decision-Making Using Reinforcement and LSH Feature Learning
Abstract
In this study, we propose an entropy-driven adaptive decision-making model designed to improve the robustness and accuracy of artificial intelligence autonomous systems under dynamic data environments. The model integrates a parameterized Bayesian structure with a reinforcement learning-based policy adjustment mechanism and a Locality-Sensitive Hashing (LSH) feature extractor. We evaluate the model using real-world datasets across three domains: medical diagnosis (Hospital Dataset-C with imbalanced binary labels), urban traffic flow (CityFlow-TR), and financial transactions (FinTech-Sim2024). Compared with a fixed Bayesian network, a deep neural model, and a basic threshold-triggered adaptation model, our system achieves a 22% improvement in diagnostic accuracy (from 0.68 to 0.90 for Disease C), 25% reduction in decision variance, and consistent performance across high-noise and largescale data. Statistical testing (t-test, p<0.05) confirms the significance of these improvements. Our findings demonstrate the effectiveness of entropy-triggered structural adjustment and adaptive policy tuning in enhancing real-time decision performance.
DOI:
https://doi.org/10.31449/inf.v49i32.8848Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika
