MIN MAX ALPHABETA PRUNING – TIC TAC TOE DEMO

Abstract

The MinMax AlphaBeta Pruning – Tic Tac Toe Demo was a concerted effort to bring the theoretical notions of AI into the practical realm of development in Unity, as a meaningful and hard record for a tutorial to game developers of all levels. This study was designed to demystify the Minimax algorithm, an adversarial search algorithm that AI employs in its simplest of forms for playing two-player, zero-sum games including tic-tac-toe and chess along with its modified version called AlphaBeta Pruning which allows the computer (AI) to significantly prune the search space by eliminating unnecessary branches (actions) that do not affect the results of the outcome. Although these results appear insignificant as a commercially driven initiative, they also indicate one thing: that even small educational artefacts can help invigorate and inspire the community and their understanding of AI-centric game design. Importantly, feedback recency—comments on Unity forum, direct messages, and support tickets— provided qualitative confirmation that the research addressed a substantial gap in current resources; especially for new developers struggling with algorithmic complexity. To summarize, I suspect the revenue path indicates the scope to improve in areas such as outreach and marketing, but, based on the rigor, clarity, and evidence of community use—the enduring value of the research is, in fact, measuring that it is a reference marker for any strategy-game AI within the Unity ecosystem.