An Integrated Expert System with a Supervised Machine Learning based Probabilistic Approach to Play Tic-Tac-Toe
Abstract
Tic-Tac-Toe, also known as Noughts and Crosses, is a widely popular game among people of all ages. In recent times, due to the rapid development of Artificial Intelligence (AI) based algorithms, AI in Games has become an interesting topic for research in both academia and industry. Due to the complicated yet competent nature of AI algorithms, the design and implementation of such AI-driven approaches in games are challenging and time intensive. In this regard, we propose a supervised Machine Learning (ML)-based approach that contributes in designing an innovative and less complex Tic-Tac-Toc expert system. Integrating AI and ML in the solution process will lead the concerned community toward a more lightweight and computationally efficient systems for playing games. In this study, we propose a novel algorithmic solution by combining an ensemble-based boosting approach and rule-based inference to build a probabilistic expert system that strategically chooses the best optimal move for next possible state of the game. A benchmark dataset containing 255,168 unique game states of Tic Tac Toe was utilized at training stage. The proposed strategy is able to successfully settle a draw against never-loosing MiniMax algorithm in 18 standard test cases.
