![]() ![]() However we can generalize this approach to make it work with other board games such as Chess or Go. Of course, the set of strategic rules we've discussed at the top is specifically tailored to the game of Tic-Tac-Toe. Note that for the rest of this post we'll only use simplified game tree examples to save screen space The following illustration shows a simplified version of such a game tree: While doing that you're building up a game tree of all the possible moves you and your opponent would play. You do this by pretending that you've played a given move and then continue playing the game until the end, alternating between the X and O player. The "only" thing you need to do is to look at the current game state you're in and play simulations through all the potential next moves which could be played. These modes and their respective actions are basically the only strategies you need to follow to win the game of Tic-Tac-Toe. Play a move which prevents your opponent from setting up a future winning situation in the next round.Play a move which prevents your opponent from winning in the next round (if possible).Play a move which sets up a future winning situation.Play a move which will cause an immediate win (if possible).Generally speaking there are 2 modes you can operate in when determining the next move you want to play: What's the best move you should pick in any given situation? While playing you're wondering what the optimal strategy might be. Let's imagine that you're playing some games of Tic-Tac-Toe with your friends. We'll also shed some light into the computational challenges we'll face and how to handle them via performance optimization techniques. After that we'll see how Minimax and MCTS can be used in modern game implementations to build sophisticated Game AIs. We'll start our journey into tree search algorithms by discovering the intuition behind their inner workings. In this blog post we'll discuss 2 famous tree search algorithms called Minimax and Monte Carlo Tree Search (abbreviated to MCTS). One such family of algorithms leverages tree search and operates on game state trees. ![]() To be precise there are a couple of algorithms which can be utilized to predict the best possible moves in games such as Tic-Tac-Toe, Connect Four, Chess and Go among others. ![]() Is there such an algorithm that will show you how you can defeat your opponent at any given time? You might've wondered if there's a certain strategy you can exploit that lets you win all the time (or at least force a draw). Do you remember your childhood days when you discovered the infamous game Tic-Tac-Toe and played it with your friends over and over again? ![]()
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