AI-Powered Smart & Adaptive Online Chess

Udayagiri Sanjay; Pothamshetti Nithin Kumar; Palugula Manuteja; Kunta Sai Snehith; Venna Ambica

doi:10.2991/978-94-6463-940-7_9

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AI-Powered Smart & Adaptive Online Chess

Authors

Udayagiri Sanjay¹^{, *}, Pothamshetti Nithin Kumar², Palugula Manuteja³, Kunta Sai Snehith⁴, Venna Ambica⁵

¹Student, Dept IT, Vignana Bharathi Institute of Technology, Hyderabad, India

²Student, Dept IT, Vignana Bharathi Institute of Technology, Hyderabad, India

³Student, Dept IT, Vignana Bharathi Institute of Technology, Hyderabad, India

⁴Student, Dept IT, Vignana Bharathi Institute of Technology, Hyderabad, India

⁵Assistant Professor, Dept IT, Vignana Bharathi Institute of Technology, Hyderabad, India

^*Corresponding author. Email: sanjayvudayagiri30@gmail.com

Corresponding Author

Udayagiri Sanjay

Available Online 31 December 2025.

DOI: 10.2991/978-94-6463-940-7_9 How to use a DOI?
Keywords: AI Chess; Deep Reinforcement Learning; Neural Networks; Minimax; Adaptive Gameplay; Game Strategy
Abstract: This research proposes the design of an AI-powered online chess system that combines classical search algorithms with modern machine learning techniques to create an adaptive and intelligent gameplay environment. The model integrates the Minimax algorithm with Alpha-Beta pruning, reinforcement learning, and a deep neural network trained on grandmaster-level datasets. Additionally, pre-trained chess engines such as Stockfish an are employed for benchmarking and move validation. The system supports real-time move prediction, adaptive difficulty adjustment, and player-centric strategic analysis. A central research question guiding this work is: Can a hybrid system combining classical search with reinforcement learning outperform traditional engines in adaptability against diverse playstyles? Results from experimental testing demonstrate competitive accuracy, reduced response time, and dynamic adaptation to human strategies, making the system suitable for both casual players and advanced learners.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
Series: Advances in Intelligent Systems Research
Publication Date: 31 December 2025
ISBN: 978-94-6463-940-7
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-940-7_9 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Udayagiri Sanjay
AU  - Pothamshetti Nithin Kumar
AU  - Palugula Manuteja
AU  - Kunta Sai Snehith
AU  - Venna Ambica
PY  - 2025
DA  - 2025/12/31
TI  - AI-Powered Smart & Adaptive Online Chess
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 84
EP  - 93
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_9
DO  - 10.2991/978-94-6463-940-7_9
ID  - Sanjay2025
ER  -

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