Assisting Generative AI Processes Based on Automatic Reasoning

Authors

  • Radu Bucea Manea Tonis Danubius University

Keywords:

AI, MiniMax, AJAX

Abstract

Generative AI processes can be significantly improved by incorporating automatic reasoning techniques, such as the MiniMax algorithm, commonly employed in decision-making contexts like game theory. Functional programming prioritizes immutability and first-class functions, while logic programming centers around declarative problem-solving through the use of rules and facts. Also, AJAX requests are essential for facilitating asynchronous data exchange between the client and server, enabling real-time updates and smooth interactions in AI-driven applications.

References

Sengar, S.S., Hasan, A.B., Kumar, S.(2024). Generative artificial intelligence: a systematic review and applications. Multimed Tools Appl. https://doi.org/10.1007/s11042-024-20016-1

Wu, J., Zhu, J., & Liu, Y. (2025). Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research. arXiv [Cs.AI]. Retrieved from http://arxiv.org/abs/2502.04644

Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. doi:10.1016/j.caeai.2021.100017

Arthur I. M.(2019), 10 Ian Goodfellow's Generative Adversarial Networks: AI Learns to Imagine, in The Artist in the Machine: The World of AI-Powered Creativity , MIT Press, 2019, pp.87-98.

Mills, O.(2023), Inside GANs: Understanding the duel of generator and discriminator, URL: https://oliviermills.com/articles/inside-gans-understanding-duel-generator-discriminator

de Souza, V. L., Marques, B. A. D., Batagelo, H. C., & Gois, J. P. (2023). A review on Generative Adversarial Networks for image generation. Computers & Graphics, 114, 13–25. doi:10.1016/j.cag.2023.05.010

Preeti, Kumar, M., & Sharma, H. K. (2023). A GAN-Based Model of Deepfake Detection in Social Media. Procedia Computer Science, 218, 2153–2162. doi:10.1016/j.procs.2023.01.191

Gandhi, V. (2015). Chapter 2 - Interfacing Brain and Machine. In V. Gandhi (Ed.), Brain-Computer Interfacing for Assistive Robotics (pp. 7–63). doi:10.1016/B978-0-12-801543-8.00002-8

Dat, M. (2024). StockGPT: A GenAI Model for Stock Prediction and Trading. URL: https://arxiv.org/html/2404.05101v1

Mienye, I.D., Swart, T.G. (2025). Deep Autoencoder Neural Networks: A Comprehensive Review and New Perspectives. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-025-10260-5

Jiang, Y., Ma, X., & Li, X. (2025). Towards virtual sample generation with various data conditions: A comprehensive review. Information Fusion, 117, 102874. doi:10.1016/j.inffus.2024.102874

Guo, Z., Lang, J., Huang, S., Gao, Y., Ding, X. (2025). A Comprehensive Review on Noise Control of Diffusion Model. URL: https://arxiv.org/html/2502.04669v1

Zain ul, A..(2023). How OpenAI’s DALL-E works? URL. https://medium.com/@zaiinn440/how-openais-dall-e-works-da24ac6c12fa

Bratko, I. (1990). PROLOG Programming for Artificial Intelligence (2nd ed.). USA: Addison-Wesley Longman Publishing Co., Inc.

Shahu, A.(2024). Mastering the Art of Functional JavaScript: Immutability, Pure Functions, and Beyond, URL: https://blogs.perficient.com/2024/03/04/mastering-the-art-of-functional-javascript-immutability-pure-functions-and-beyond/

www1 - radubm1/TicTacToe at ReactTicTac

www2 - jaunerc/minimax-prolog: minimax in SWI Prolog for Tic Tac Toe

www3 - reactide/reactide: Reactide is the first dedicated IDE for React web application development.

www4 - reactide/example at master · reactide/reactide

Downloads

Published

2025-06-15

Issue

Section

Technology in Education