Randomised fast no-loss expert system to play tic-tac-toe like a human

Abstract

This study introduces a blazingly fast, no-loss expert system for tic-tac-toe using decision trees called T3DT, which tries to emulate human gameplay as closely as possible. It does not make use of any brute force, minimax, or evolutionary techniques, but is still always unbeatable. To make the gameplay more human-like, randomisation is prioritised and T3DT randomly chooses one of the multiple optimal moves at each step. Since it does not need to analyse the complete game tree at any point, T3DT is exceptionally faster than any brute force or minimax algorithm, this has been shown theoretically as well as empirically from clock-time analyses in this study. T3DT also does not need the data sets or the time to train an evolutionary model, making it a practical no-loss approach to play tic-tac-toe.

Aditya Jyoti Paul

Technical Program Manager and CV/AI Researcher

My work makes machines smarter, secure and more accessible. I’m passionate about research, teaching and blogging. Outside academia, I love travel, music, reading and meeting new people!