AI Applications for Solutions to Popular Games: A Comparative Analysis of Heuristic, Search Algorithms, and Machine Learning

Abstract

Artificial Intelligence (AI) encompasses a broad spectrum of techniques, ranging from classical search algorithms and heuristics to more advanced machine learning and deep learning models. To find the solution for popular game problems, the choice of the appropriate AI method depends on the nature of the problem being addressed. For well-defined, rule-based problems such as turn-based games like Tic-Tac-Toe or Connect 4, classical algorithms like Minimax and Monte Carlo Tree Search (MCTS) are highly effective due to their ability to explore finite state spaces and make optimal decisions. However, for more dynamic and complex problems, such as those encountered in sports analytics or real-time decision-making, advanced machine learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks are more effective to capture intricate patterns and handle unstructured data. Despite the growing capabilities of deep learning, simpler methods like heuristic-based optimization and statistical techniques such as curve fitting and K-nearest neighbors (KNN) can still be highly effective in certain domains. This paper explores the strengths, weaknesses, and effectiveness of various AI techniques and develops guidelines on how to select the right approach based on problem complexity and data characteristics. Through case studies of different popular games, we demonstrate how different AI methods can be applied to real-world problems, and how hybrid approaches can often provide the best solution for complex tasks.