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

TL;DR

This paper presents T3DT, a fast, no-loss Tic Tac Toe expert system that emulates human-like gameplay through randomization, avoiding brute force and minimax methods, and demonstrating superior speed and practicality.

Contribution

Introduces T3DT, a novel decision tree-based Tic Tac Toe system that is faster, more human-like, and does not require training or exhaustive game tree analysis.

Findings

T3DT is faster than traditional minimax algorithms.

T3DT always wins or draws, ensuring no loss.

Empirical clock-time analysis confirms T3DT's efficiency.

Abstract

This paper introduces a blazingly fast, no-loss expert system for Tic Tac Toe using Decision Trees called T3DT, that 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. In order to make the gameplay more human-like, randomization is prioritized 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 paper. T3DT also doesn't need the data sets or the time to train an evolutionary model, making it a practical no-loss approach to play Tic Tac Toe.