In each playout, the game is played out to the very end by selecting moves at random. The application of Monte Carlo tree search in games is based on many playouts. The focus of MCTS is on the analysis of the most promising moves, expanding the search tree based on random sampling of the search space. Monte Carlo Tree Search (MCTS) is a search technique in the field of Artificial Intelligence (AI).

If a player is sent to such a board, then that player may play in any other board. Once a local board is won by a player or it is filled completely, no more moves may be played in that board. If a move is played so that it is to win a local board by the rules of normal tic-tac-toe, then the entire local board is marked as a victory for the player in the global board. O can then play in any one of the nine available spots in that local board, each move sending X to a different local board. For example, if X played in the top right square of their local board, then O needs to play next in the local board at the top right of the global board. This move “sends” their opponent to its relative location. The game starts with X playing wherever they want in any of the 81 empty spots. Players take turns playing in the smaller tic-tac-toe boards until one of them wins in the larger tic-tac-toe board. Ultimate tic-tac-toe is a board game composed of nine tic-tac-toe boards arranged in a 3 × 3 grid.

Ultimate tic tac toe boards