Received: 12 November 2025; Revised: 19 December 2025; Accepted: 21 December 2025; Published Online: 22 December 2025.
J. Inf. Commun. Technol. Algorithms Syst. Appl., 2025, 1(3), 25315 | Volume 1 Issue 3 (December 2025) | DOI: https://doi.org/10.64189/ict.25315
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
A Hybrid Search–Learning Framework for Artificial
Intelligence in Board Games
Ganesh Jadhav,
1,*
Parikshit N. Mahalle,
2
Tejas Desale,
1
Tejas Deshmukh,
1
Shreya Dhaytonde,
1
Swapnil Hajare
1
and Varad
Gheware
1
1
Department of Information Technology, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India
2
Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India
*Email: jadhavganesh874@gmail.com (G. Jadhav)
Abstract
Artificial Intelligence (AI) has played a transformative role in the evolution of board games by enabling machines to
exhibit strategic reasoning, long-term planning, and adaptive decision-making. Board games such as Chess, Go, and
Checkers provide well-defined environments with complex state spaces, making them ideal benchmarks for
evaluating AI techniques. Early rule-based systems relied heavily on handcrafted heuristics and exhaustive search
strategies, while recent advances leverage deep neural networks and reinforcement learning to achieve superhuman
performance. This paper presents a hybrid study that combines a focused review of classical and modern AI
approaches in board games with the design and evaluation of a proposed hybrid search–learning architecture. The
proposed system integrates Minimax, Monte Carlo Tree Search (MCTS), and deep reinforcement learning within a
modular four-layer framework to achieve scalability, adaptability, and efficient real-time decision-making. Extensive
experimental evaluation on Chess, Go, and Checkers demonstrates that the proposed architecture achieves improved
win rates, reduced inference latency, and measurable Elo rating gains compared to traditional and baseline AI
systems. Beyond gaming, the findings highlight the broader applicability of board-game AI techniques in strategic
planning, optimization, and human-AI interaction domains.
Keywords: Artificial intelligence; Board games; Reinforcement learning; Game theory; Monte Carlo Tree Search;
Minimax algorithm.
1. Introduction
Artificial Intelligence (AI) in board games refers to the development of computational systems capable of analyzing
game states, reasoning strategically, and selecting optimal moves in structured, rule-based environments.
[1-3]
Board
games have long served as controlled experimental platforms for AI research due to their deterministic rules, discrete
action spaces, and measurable performance outcomes.
[4,5]
These properties make board games particularly suitable for
studying algorithmic decision-making, adversarial reasoning, and long-term planning.
[6]
Board games are considered
ideal testbeds for AI research because they combine strategic depth with formal mathematical structure.
[2,7,8]
Unlike
real-world environments that are noisy and unpredictable, board games offer complete observability, clearly defined
objectives, and repeatable experimental conditions.
[9]
This allows researchers to rigorously evaluate AI algorithms,
compare performance metrics, and reproduce results. Consequently, advancements in board-game AI often translate
into broader applications, including scheduling, logistics optimization, robotics planning, and autonomous decision-
making systems.
[10]
The historical development of AI in board games has progressed through several distinct phases.
Early approaches relied on rule-based systems and exhaustive search techniques, most notably the Minimax algorithm
enhanced with Alpha–Beta pruning.
[1,11,12]
A landmark achievement occurred in 1997 when IBM’s Deep Blue defeated
world chess champion Garry Kasparov, demonstrating the effectiveness of brute-force search combined with expert-
crafted evaluation functions.
[13,14]
Subsequent research introduced Monte Carlo Tree Search (MCTS), which enabled
efficient exploration of large game trees using probabilistic sampling, particularly benefiting games with high
branching factors such as Go.
[9,15]
The integration of machine learning marked a significant shift in board-game AI.
[16]
Google DeepMind’s AlphaGo
(2016) combined deep neural networks with MCTS and reinforcement learning to defeat a world champion Go player,
a feat previously considered decades away.
[6,17]
This paradigm evolved further with AlphaZero and MuZero, which
eliminated reliance on human game data and learned strategies entirely through self-play.
[6,10]
In parallel, advances in
imperfect-information games, such as Libratus and Pluribus in poker, demonstrated that AI could reason under
uncertainty and incomplete information.
[7]
Recent trends in board-game AI emphasize the use of convolutional neural
networks, deep reinforcement learning, and hybrid systems that combine learning-based models with classical search
techniques.
[10,17-20]
While these approaches achieve exceptional performance, they also introduce challenges related to
computational cost, scalability, explainability, and real-time deployment.
[4]
Many state-of-the-art systems require
extensive centralized computing resources, making them challenging to deploy in practical or resource-constrained
environments [6]. Despite significant progress, a key research gap remains in developing AI systems that balance
strategic strength with computational efficiency and deployment feasibility.
[2,9]
Most existing solutions prioritize peak
performance over scalability, latency, and operational cost. This motivates the need for hybrid architectures that retain
the strategic advantages of deep learning while controlling inference complexity and enabling modular deployment.
[11]
This study aims to address this gap by proposing a hybrid search–learning architecture for AI in board games. The
primary contribution of this work lies in designing a modular four-layer framework that integrates Minimax, MCTS,
and deep reinforcement learning, and evaluating its performance across multiple board games using comprehensive
experimental metrics.
[1,10]
The key contributions of this study are as follows:
1. This study provides a concise review of the evolution of AI techniques in board games, from classical rule-based
systems to modern self-learning models.
2. A hybrid four-layer AI architecture combining search algorithms and deep reinforcement learning is proposed for
scalable board-game intelligence.
3. The proposed system is experimentally evaluated on Chess, Go, and Checkers using multiple performance metrics,
including win rate, Elo rating, and inference latency.
4. The study demonstrates that efficient hybrid models can achieve strong strategic performance while maintaining
real-time deployment viability.
2. Related work
2.1 Early artificial intelligence in board games
The application of artificial intelligence to board games dates back to the earliest days of AI research, where games
were viewed as simplified models of strategic reasoning.
[1]
Classical approaches relied on deterministic search
algorithms such as Minimax and Alpha–Beta pruning, supported by handcrafted evaluation functions.
[1]
Deep Blue
demonstrated the effectiveness of brute-force search combined with domain-specific heuristics and parallel
computation, establishing board games as credible benchmarks for AI performance.
[13]
However, such systems required
extensive expert knowledge and were not adaptable, as their intelligence was limited to predefined rules and evaluation
metrics.
[1]
2.2 Monte Carlo Tree Search and probabilistic methods
To overcome the limitations of exhaustive search in large state spaces, Monte Carlo Tree Search (MCTS) emerged as
a powerful alternative.
[9]
MCTS enabled efficient exploration of game trees through randomized simulations and
statistical decision-making.
[2]
This approach proved particularly successful in games with high branching factors, such
as Go, where traditional Minimax-based methods were computationally infeasible.
[17]
Early MCTS-based systems
demonstrated improved scalability and decision quality but still relied on handcrafted rollout policies and lacked
learning capability, limiting their long-term adaptability.
[9]
2.3 Hybrid architectures and generalization
Recent research has focused on hybrid architectures that combine classical search algorithms with learning-based
models.
[21]
Systems such as Leela Zero and other AlphaZero-inspired frameworks highlight the benefits of integrating
neural policy and value networks with MCTS.
[6]
While these hybrid approaches improve adaptability and strategic
depth, they often prioritize peak performance over deployment efficiency.
[10]
Additionally, most existing studies focus
on single-game optimization rather than designing generalized, modular frameworks capable of supporting multiple
board games with minimal redesign.
[22]
2.4 Deep Learning and Reinforcement Learning Approaches
The integration of deep neural networks with reinforcement learning marked a significant shift in board-game AI.
AlphaGo combined convolutional neural networks with MCTS and reinforcement learning to achieve superhuman
performance in Go.
[23]
Subsequent systems such as AlphaZero and MuZero eliminated reliance on human game data,
learning optimal strategies entirely through self-play.
[6]
These systems demonstrated remarkable generalization across
multiple games, including Chess, Go, and Shogi. Despite their success, such approaches require massive computational
resources, complex distributed training pipelines, and are often impractical for real-time or resource-constrained
environments.
[10]
2.5 Research gap and motivation
Although significant progress has been made in board-game AI, several limitations remain. Many state-of-the-art
systems require extensive centralized computation, suffer from high inference latency, and lack deployment flexibility.
Furthermore, limited attention has been given to modular architectures that balance performance, scalability, and
operational cost. This research addresses these gaps by proposing a hybrid search–learning framework designed for
efficient real-time inference, scalability across multiple board games, and practical deployment in distributed
environments.
3. Methodology
3.1 System architecture
For effective decision-making, scalability, and adaptability across a variety of board games, including chess, go, and
checkers, the suggested system uses a modular four-layer architecture:
Input Layer: The input layer records the game’s current state and potential player movements. A matrix or vector
encoding positions, pieces, and legal actions is used to represent each game state. Normalization, board encoding,
and move generation according to game rules are examples of data preprocessing.
Preprocessing Layer: The processing layer uses strategic search algorithms such as Minimax and Monte Carlo Tree
Search (MCTS). Minimax mimics every possible move to determine the optimal strategy, assuming both players
perform at their peak.
[24]
By utilizing probabilistic sampling to estimate the most promising actions in large state
spaces, MCTS strikes a balance between exploration and exploitation through the Upper Confidence Bound (UCB1)
policy.
[25]
Learning Layer: For adaptive gameplay, this layer combines reinforcement learning (RL) and neural network policies.
The value network calculates the strength of the board position, and the policy network forecasts the probabilities of
moves.
[25]
Through reward-based feedback, reinforcement learning (through self-play) updates these networks,
gradually enhancing the AI’s approach.
Decision Layer: The last layer uses combined insights from the search and learning modules to assess all move
outcomes and choose the best course of action. To facilitate human-AI interaction, the decision layer can also predict
strategies, analyze opponent patterns, and dynamically adjust difficulty. Fig. 1 illustrate architecture for proposed
hybrid search–learning system architecture for AI in board games.
Fig. 1: Proposed hybrid search–learning system architecture for AI in board games (a) conceptual layered architecture; (b)
operational workflow.
3.2 Dataset and annotation
Using open-source platforms such as OpenSpiel and frameworks similar to AlphaZero, the experimental environment
replicates standard board games, such as chess, go, and tic-tac-toe. Every game state has organized elements like:
Encoding of piece position: Put on legality masks.
Signals of rewards for victories, defeats, and draws.
Self-playing games serve as the training data, enabling the AI to iteratively improve its tactics without human
assistance. To aid in policy learning, annotations include state-action-reward tuples (s, a, r).
3.3 Learning and optimization
The reinforcement learning process uses a policy gradient method to update network parameters to maximize long-
term rewards. Mathematically,
(1)
The loss function combines policy improvement and value accuracy:
(2)
where L
entropy
encourages exploration and prevents premature convergence.
3.4 Search and decision integration
The hybrid decision engine fuses neural predictions with search-based evaluations. For each move:
MCTS generates simulations to estimate move quality.
The neural policy suggests a probability distribution over legal moves.
A weighted fusion mechanism selects the final move.
(3)
where α is dynamically tuned based on confidence in search results.
θ denotes the policy network parameters, r represents the reward signal obtained from game outcomes, Lentropy is an
entropy regularization term encouraging exploration, and α controls the trade-off between neural prediction and search-
based evaluation.
3.5 Human-AI interaction system
The final module offers an adaptive gameplay interface where the difficulty varies based on player performance.
[7]
High confidence AI decisions are made autonomously; however, when there is uncertainty, heuristic evaluation or
human feedback loops are activated. For players of all skill levels, this promotes engaging and equitable gameplay.
3.6 Scalability and deployment viability
The scalability of the proposed AI architecture is crucial because contemporary board games like Checkers, Go, and
Chess involve a vast Number of possible game states, ranging from 10
47
in Chess to over 10
170
in Go. The system
uses a modular four-layer design with Input, Processing, Learning, and Decision layers to manage this complexity.
Multiple simulations and evaluations can run concurrently across distributed computing nodes, thanks to the
independent modules that each layer can scale horizontally.
Monte Carlo Tree Search (MCTS) and parallelized Minimax algorithms are used in the processing layer to spread
search calculations among several GPU and CPU clusters. This dramatically increases the system’s decision-making
efficiency by allowing it to examine millions of game positions at once.
[23]
To continuously update neural network
weights via asynchronous gradient exchange mechanisms, the learning layer employs reinforcement learning agents
that train through extensive self-play. In contrast to conventional serial learning techniques, this design reduces
bottlenecks and speeds up model convergence.
For real-world applicability, this system was deployed on a distributed GPU cluster using PyTorch Lightning and
Ray RLlib frameworks. Stress testing showed that the architecture can support more than 5,000 concurrent self-play
simulations at an average decision latency of 0.21 seconds per move.
Comparative benchmarks against traditional single-node implementations revealed a 12× improvement in
computational throughput and a 65% reduction in average training time. The architecture is further cloud-compatible,
allowing the deployment of containerized models using Docker and Kubernetes for both research and production
environments. The model natively supports real-time inference for online board game platforms, allowing adaptive
gameplay experiences based on player performance.
[23]
Future scaling will include support for federated reinforcement
learning, in which multiple systems train locally on different game variants while sharing model parameters without
exchanging any sensitive data.
3.7 Comparative analysis with current systems
This section compares the proposed AI-in-board-games architecture with representative existing systems and
approaches. We evaluate across several axes: core approach, learning paradigm, search strategy, compute requirements,
adaptability to new games, real-time inference capability, and typical strengths and limitations.
3.7.1 Qualitative comparison
Classical search-based engines (e.g., traditional chess engines, like early Deep Blue or Stockfish variants) rely
primarily on handcrafted evaluation functions combined with deep, deterministic search (alpha-beta/Minimax). These
systems are high-speed at inference, excel when strong heuristics exist, but require extensive domain engineering and
do not learn from self-play.
[25]
MCTS-based systems (e.g., early AlphaGo variants) are robust in large-branching-factor
games thanks to probabilistic rollouts and tree search, but can be computationally intensive at inference time.
[23]
Modern
hybrid self-play systems (AlphaZero/Leela Zero family) that combine neural policy/value networks with MCTS
achieve superior generalization and learning capability across multiple games at the cost of heavy training compute
(large-scale GPUs/TPUs) and complex distributed training pipelines.
The proposed architecture is hybrid in nature but focuses on modularity and deployment readiness: It combines
efficient parallelized search with lightweight neural policy/value models for fast inference and distributed self-play
training with asynchronous updates. Compared to purely search-based engines, the proposed system enhances
adaptability and enables the discovery of novel strategies without human-crafted heuristics. Compared to AlphaZero-
like systems, this work aims to reduce operational cost by tuning the trade-off between neural compute and the search
budget and by supporting federated and distributed training options that lower the centralized compute load.
3.8 Experimental setup and evaluation
3.8.1 Dataset characteristics
Our evaluation dataset comprises over 1.2 million self-play game records generated during a six-week training period
(January–March 2025) using the proposed AI framework. The dataset includes structured state-action-reward tuples
(s, a, r) across three major board games: Chess, Go, and Checkers, each selected to represent increasing levels of game
complexity and branching factors.
For each game, multiple configurations were tested using different search depths, rollout limits, and exploration
parameters to ensure diverse gameplay coverage. Each self-play session was logged with metadata, including move
sequence, policy probabilities, value estimations, and game outcomes (win, loss, draw).
[25]
The dataset also integrates evaluation logs from matches against benchmark engines such as Stockfish (for Chess),
GnuGo (for Go), and Chinook (for Checkers). These match records were used to analyze the system’s adaptability and
generalization capabilities. Data were preprocessed into normalized tensor representations for input to neural policy
and value networks, while rewards were encoded on the [−1, 1] scale corresponding to terminal game outcomes.
Stockfish v16 (level 20), GnuGo v3.8, and Chinook v1.0 were used as benchmark opponents. Experiments were
conducted on NVIDIA RTX-series GPUs with multi-core Intel Xeon CPUs and 64 GB RAM per node.
3.8.2 Evaluation metrics
We employ a comprehensive set of metrics spanning both algorithmic performance and operational efficiency to assess
the AI system’s effectiveness:
1. Win Rate: Percentage of games won by the AI system against benchmark opponents across multiple difficulty
levels.
2. Macro-F1: Macro-F1 is computed over multi-class action (move) predictions, where each legal move is treated
as a separate class, to evaluate balanced decision-making performance under imbalanced action distributions.
3. Move Accuracy: Agreement rate of the model’s chosen move with the optimal move determined by a reference
engine or expert dataset.
4. Average Search Depth: The mean Number of moves explored in the decision tree before final action selection,
indicating computational efficiency.
5. Policy Confidence: Average softmax probability assigned to the executed move, reflecting decision certainty.
6. Reward Convergence: Difference between predicted and actual rewards over training epochs, showing learning
stability.
7. Elo Rating Improvement: Relative skill increases over training time, computed using standard Elo ranking
formulas.
8. Processing Time per Move: Average latency (in seconds) required to generate a single move recommendation
under standard inference conditions.
These metrics collectively provide a balanced evaluation of the proposed system, covering accuracy, strategy
consistency, efficiency, and generalization. Win rate and move accuracy capture playing strength, while convergence
and latency metrics validate scalability and real-time viability for deployment in competitive gaming platforms.
4. Results and analysis
Various training experiments have been conducted on the Chess, Go, and Checkers environments using self-play and
benchmark datasets to assess the performance of the proposed AI-based board game system.
[25]
Model training for 50
epochs with a batch size of 128, a learning rate of 0.0005, and the Adam optimizer was conducted. Reinforcement
learning agents were trained with policy and value networks integrated with MCTS.
4.1 Model training and convergence
As shown in Fig. 2, both the training loss and policy entropy decrease steadily over successive epochs, indicating
stable learning behavior. The reduction in policy entropy reflects increasing confidence in action selection, while
convergence of the loss curve after approximately 40 epochs confirms training stability. These results validate the
effectiveness of integrating reinforcement learning with MCTS-based self-play.
4.2 Gameplay interface and workflow
The system features a user-friendly gameplay interface for playing against AI at different difficulty levels. Real-time
visualization, including board states, move probabilities, and predicted outcomes, enhances interactivity. The admin
console can monitor AI performance metrics, log matches, and model opponents.
4.3 Assessing gameplay performance
The best win rate of the trained AI is 92.4% against traditional Minimax-based opponents and 87.1% against advanced
heuristic engines. During human-level simulations, the model consistently generated optimal move sequences,
demonstrating substantial strategic depth. Missteps were mainly observed in endgame positions with high branching
complexity.
Fig. 2: Training and validation performance of the model across epochs, a) training and validation accuracy, b) training and
validation loss, c) training loss and policy entropy.
4.4 Comparative performance analysis
Table 1 summarizes the performance comparison across different algorithms and systems. The proposed hybrid model
achieved a win rate of 92.4 and a Macro-F1 score of 0.863, outperforming all baselines. In addition, the proposed
model recorded an average move latency of 0.38 seconds, showing real-time viability without compromising accuracy.
Operational impact assessment during deployment testing further highlights the scalability of the proposed approach.
The system reduced computational load by 58% through asynchronous self-play and enabled parallel execution of
over 5,000 concurrent simulations.
[10]
Table 2 summarizes the key performance metrics.
Table 1: Performance comparison across game AI methods.
Method
Win Rate
Macro-F1
Elo Gain
Latency
Minimax (Depth 5)
0.782
0.731
+152
0.42
MCTS (Baseline)
0.843
0.796
+224
0.68
Policy Network Only
0.801
0.762
+187
0.21
AlphaZero-Style Hybrid
0.911
0.845
+278
0.53
Proposed Hybrid System
0.924
0.863
+316
0.38
Table 2: Operational Impact: Baseline vs. Proposed AI System.
Metric
Baseline
Proposed System
Improvement
Training time
120
68
43% faster
Decision latency
0.92
0.38
59% reduction
Elo Rating
2350
2666
+13.4%
Game simulations
1200
5100
4.2× increase
Resource utilization
88%
64%
27% efficiency gain
4.5 Ablation study
An ablation study was conducted to assess the individual contributions of the three significant components of the
proposed system: the policy network, the value network, and the Tree of Search (ToS), which represents the structured
exploration of game states during decision evaluation. The results show that removing the MCTS-based search
component leads to a noticeable decline in win rate, while excluding the value network reduces prediction stability
and overall decision accuracy. These findings indicate that each element plays a complementary role in the decision-
making process. The combined integration of neural policy learning and structured search enables the system to
achieve a balanced trade-off between strategic accuracy, inference speed, and computational efficiency, thereby
confirming the effectiveness of the proposed hybrid architecture for game-playing tasks. Table 3 shows Ablation study
results.
Table 3: Ablation study results.
Configuration
Win Rate
Macro-F1
Full model (Hybrid)
0.924
0.863
Without MCTS
0.856
0.809
Without value
network
0.874
0.818
Policy only
0.801
0.762
5. Discussion
5.1 Technical achievements
The proposed hybrid architecture integrates the best of search-based algorithms and deep reinforcement learning to
enable high-accuracy decision-making in games such as chess and Go. It achieves strong adaptability with reduced
inference time and facilitates learning through distributed self-play. The ablation study results shown in Table 3 include
the complete model (hybrid), which achieves a high win rate compared to other configurations and also performs better
across different parameters.
5.2 Operational benefits
The deployment results in faster move prediction, reduced computation overhead, and seamless scaling across multiple
games. Automation of self-play minimizes human effort, while a modular, cloud-ready design supports real-time
gameplay and adaptive difficulty adjustment.
5.3 Challenges and limitations
Key challenges include high computational requirements for large state spaces, maintaining real-time response across
complex searches, and neural strategies that are only partially interpretable. Generalization and human-like creativity
remain points of ongoing research.
5.4 Future work
Several promising research directions emerge:
Transfer Learning: Extending trained models to new and unseen board games with minimal retraining effort.
Federated Learning and Reinforcement learning: Enable decentralized self-play training across distributed systems,
while preserving data privacy.
Adaptive Opponent Modeling: Designing AI agents that can dynamically adapt strategies based on players’ behavior
and skills.
6. Conclusion
The evolution of artificial intelligence in board games reflects the broader progress of AI research, transitioning from
early rule-based systems and brute-force search to advanced self-learning architectures capable of strategic reasoning.
Milestones such as Deep Blue, AlphaGo, and AlphaZero illustrate how board games have consistently served as
benchmarks for measuring AI capabilities and innovation. This work contributes to this trajectory by proposing a
hybrid search–learning architecture that integrates classical algorithms with deep reinforcement learning. The modular
four-layer design enables adaptability across multiple board games while maintaining efficient inference and scalable
training. Experimental results on Chess, Go, and Checkers demonstrate that the proposed system achieves strong
performance in terms of win rate, Elo improvement, and operational efficiency. The technical strengths of the proposed
approach include reduced computational overhead, real-time decision-making capability, and deployment readiness in
distributed environments. By balancing neural computation with search depth, the system achieves a favorable trade-
off between accuracy and latency. Beyond board games, the findings of this study have broader implications for AI
research in strategic planning, optimization, and human-AI collaboration. Techniques developed for structured game
environments can be extended to real-world applications, such as autonomous systems, logistics, and decision-support
tools. Future research directions include improving model interpretability, further reducing computational cost, and
extending the framework to imperfect-information and multi-agent environments. Continued exploration of hybrid
and federated learning approaches may further enhance the scalability and practical applicability of AI systems inspired
by board games.
Acknowledgment
The authors express their sincere gratitude to the Department of Information Technology, Vishwakarma Institute of
Technology, Pune, for their valuable guidance and support throughout this work.
Conflict of Interest
There is no conflict of interest.
Supporting Information
Not applicable
Use of artificial intelligence (AI)-assisted technology for manuscript preparation
The authors confirm that no artificial intelligence (AI)-assisted technology was used to write or edit the manuscript,
and that no images were manipulated using AI.
References
[1] S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education, 2021
[2] C. B. Browne; E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S.
Samothrakis, A survey of Monte Carlo Tree Search methods, IEEE Transactions on Computational Intelligence and
AI in Games, 2012, 4, 1-43, March 2012, doi: 10.1109/TCIAIG.2012.2186810.
[3] A. del Bosque, P. Fernández-Arias, G. Lampropoulos, D. Vergara, The role of artificial intelligence in gaming,
Applied Sciences, 2025, 15, 12358, doi: 10.3390/app152312358.
[4] I. Szita, Reinforcement learning in games. In: Wiering, M., van Otterlo, M. (eds) Reinforcement learning.
adaptation, learning, and optimization, Springer, Berlin, Heidelberg, 2012, 12, doi: 10.1007/978-3-642-27645-3_17.
[5] T. R. Robbins, The games AIs play - a comprehensive review, Journal of Applied Business and Economics, 2025,
27, doi: 10.33423/jabe.v27i6.7948.
[6] D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel,
T. Lillicrap, K. Simonyan, D. Hassabis, A general reinforcement learning algorithm that masters chess, shogi, and go
through self-play, Science, 20218, 362, 1140–1144, doi: 10.1126/science.aar6404.
[7] N. Brown, T. Sandholm, Libratus: The superhuman AI for no-limit poker, Proceedings of the Twenty-Sixth
International Joint Conference on Artificial Intelligence (IJCAI'17), AAAI Press, 2017, 5226–5228.
[8] C. Hu, Y. Zhao, Z. Wang, H. Du, J. Liu, Games for artificial intelligence research: a review and perspectives, IEEE
Transactions on Artificial Intelligence, 2024, 5, 5949-5968, doi: 10.1109/TAI.2024.3410935.
[9] L. Kocsis, C. Szepesvri, Bandit based monte-carlo planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds)
Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2006,
4212, doi: 10.1007/11871842_29.
[10] J. Schrittwieser, I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis,
T. Graepel, T. Lillicrap, D. Silver, Mastering Atari, Go, chess, and shogi by planning with a learned model, Nature,
2020, 588, 604–609, doi: 10.1038/s41586-020-03051-4.
[11] D. E. Knuth, R. W. Moore, An analysis of alpha–beta pruning, Artificial Intelligence, 1975, 6, 293–326, doi:
10.1016/0004-3702(75)90019-3.
[12] Y. Lu, W. Li, Techniques and paradigms in modern game ai systems, Algorithms, 2022, 15, 282, doi:
10.3390/a15080282
[13] M. Campbell, A. J. Hoane Jr., F. H. Hsu, Deep blue, Artificial Intelligence, 2022, 134(1–2), 57–83, doi:
10.1016/S0004-3702(01)00129-1.
[14] Monty Newborn, Deep Blue: An Artificial Intelligence Milestone, Springer New York, NY, 2003, doi:
10.1007/978-0-387-21790-1.
[15] M. Świechowski, K. Godlewski, B. Sawicki, J. Mańdziuk, Monte Carlo Tree Search: a review of recent
modifications and applications, Artificial Intelligence Review, 2023, 56, 2497–2562, doi: 10.1007/s10462-022-10228-
y.
[16] M. Świechowski, H. Park, J. Mańdziuk, K-J. Kim, recent advances in general game playing, The Scientific World
Journal, 2015, 986262, doi: 10.1155/2015/986262.
[17] D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V.
Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach,
K. Kavukcuoglu, T. Graepel, D. Hassabis, Mastering the game of Go with deep neural networks and tree search,
Nature, 2016, 529, 484–489, doi: 10.1038/nature16961.
[18] A. Liu, AI techniques in board game: A survey, Applied and Computational Engineering, 2024, 79, 49-59, doi:
10.54254/2755-2721/79/20241297.
[19] J. Hu, F. Zhao, J. Meng, S. Wu, Application of Deep Reinforcement Learning in the Board Game, 2020 IEEE
International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China,
2020, 809-812, doi: 10.1109/ICIBA50161.2020.9277188.
[20] K. Xenou, G. Chalkiadakis, S. Afantenos, Deep Reinforcement Learning in Strategic Board Game Environments.
In: Slavkovik, M. (eds) Multi-Agent Systems. EUMAS 2018. Lecture Notes in Computer Science, 2019, 11450.
Springer, Cham, doi: 10.1007/978-3-030-14174-5_16.
[21] S. Gelly, D. Silver, Combining online and offline knowledge in UCT, Proceedings of the 24th International
Conference on Machine Learning (ICML), 2007, 273-280, doi: 10.1145/1273496.127353.
[22] N. Justesen, P. Bontrager, J. Togelius, S. Risi, Deep learning for video game playing, IEEE Transactions on
Games, 2019, 12, 1–13, doi: 10.1109/TG.2019.2896986.
[23] S. Liu, J. Cao, Y. Wang, W. Chen, Y. Liu, Self-play reinforcement learning with comprehensive critic in computer
Games, Neurocomputing, 2021, 449, 207-213, doi: 10.1016/j.neucom.2021.04.006.
[24] C. Jiang, The application of artificial intelligence in board games, Proceedings of the 3rd International Conference
on Signal Processing and Machine Learning, doi: 10.54254/2755-2721/4/20230497.
[25] H. Jiang, Applications of artificial intelligence in game algorithms: history, current status, and future prospects,
Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024), doi:
10.2991/978-94-6463-512-6_45.
Publisher Note: The views, statements, and data in all publications solely belong to the authors and contributors. GR
Scholastic is not responsible for any injury resulting from the ideas, methods, or products mentioned. GR Scholastic
remains neutral regarding jurisdictional claims in published maps and institutional affiliations.
Open Access
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which
permits the non-commercial use, sharing, adaptation, distribution, and reproduction in any medium or format, as long
as appropriate credit to the original author(s) and the source is given by providing a link to the Creative Commons
License and changes need to be indicated if there are any. The images or other third-party material in this article are
included in the article's Creative Commons License, unless stated otherwise in a credit line to the material. If material
is not included in the article's Creative Commons License and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of
this License, visit: https://creativecommons.org/licenses/by-nc/4.0/.
© The Author(s) 2025
