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.

Graphical Abstract

Novelty Statement

A hybrid search–learning architecture integrating Minimax, MCTS, and deep reinforcement learning for scalable and adaptive AI in board games.