Embracing my inner child: developing a Tic-Tac-Toe game with Minimax algorithm
Tags: ai, algorithms, gamedev
Published: 2023-08-01 14:50:51

Throughout my childhood, I embarked on an ambitious journey to craft my own video games, igniting a dream that would shape my path as a software developer in the years to come.

Since an early age, my fascination with the game of chess has been unwavering. The historic clash between Kasparov and Deep Blue left an indelible mark on me, sparking a profound curiosity for artificial intelligence and its potential in the realm of gaming.

![Chess game](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/3ljbqfgqq0vigg60bxhc.png)

The remarkable strides that AI has made over the years have captivated my imagination. Witnessing the continuous advancement of AI algorithms and their ability to outperform human intellect in various strategic games has been nothing short of awe-inspiring.

Recently, I decided to explore AI algorithms in a game much simpler than chess - Tic-Tac-Toe. Though seemingly elementary, **this classic game presents intriguing challenges when played on a larger grid, such as 6x6**, with the victory condition being four symbols in a row, akin to the famous "Connect Four."

![Tic-tac-toe program snapshot](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/e1sx44rvbvtb92803b3p.png)

This game brings together classic gameplay with the strategic brilliance of the **Minimax algorithm**, making it an exhilarating challenge for players of all levels.

So, what exactly is the Minimax algorithm, and how does it enhance the gaming experience? In a nutshell, Minimax is an artificial intelligence (AI) algorithm used in two-player games, like Tic-Tac-Toe. The primary goal of Minimax is to determine the best possible move for the AI player, maximizing its chances of winning and minimizing potential losses.

The algorithm achieves this by exploring all possible moves on the game board and assigning a score to each position. It assumes that the opponent is also playing optimally, trying to minimize the AI's score. Through a process of recursive evaluation, the AI can traverse the entire game tree and ultimately make the best move at each turn.

To optimize the Minimax algorithm's performance, I employed the **alfa-beta pruning technique**. This method significantly reduces the number of game states the algorithm needs to evaluate. By intelligently pruning branches of the game tree that are unlikely to lead to better results, the algorithm becomes more efficient and capable of handling more complex game scenarios.

Additionally, I implemented **iterative depth** to control the search depth dynamically. This approach allows the AI to explore the game tree at various depths, depending on the complexity of the position. Consequently, the AI adapts to the current board state, handling deeper search depths based on the allocated maximum move time.

If you're interested in exploring the inner workings of the game and its AI, you can find the complete source code on my GitHub repository here:
- [tic-tac-toe](https://github.com/gprovenz/tic-tac-toe)

Here are a few links for you to explore the Minimax algorithm:
- [Minimax (Wikipedia)](https://en.wikipedia.org/wiki/Minimax)
- [Mini-Max Algorithm in Artificial Intelligence](https://www.javatpoint.com/mini-max-algorithm-in-ai)

Whether you're an aspiring game developer, a seasoned programmer, or simply someone looking for a fun and brain-teasing challenge, this game promises to keep you on your toes... Have fun!