AI for tic-tac-toe
In this repo I have implemented minimax, alpha-beta, alpha-beta cutoff with heuristics for the game of tic-tac-toe.
The AI (hopefully) never loses but it also doesn't always make the most challenging moves.
Alpha-beta is not really needed for the simple game but if you increase the size of the grid, then it becomes useful.
Alpha-beta cutoff explores until max depth and then uses a heuristic to evaluate the position.
Each algorithm is implemented in each own file and defines a computer_move() function which is used by game.c to play the game.
Tic.c is a standalone file to run a game using minimax.
Check makefile on how to run
The game state is a struct that contains:
- the board at the current state
- the move that will be played(and initially the last move played)
- who plays next(Player or AI)
- the depth of the state in the game tree
- and a pointer to the next gamestate
Every state generates all it’s children and then the algorithm traverses them.
TODO:
- Implement monte carlo tree search
- Randomize the list of the children because right now the AI plays the first not loosing move it finds
- Make more challenging and promising moves. For example play the move that leads to most wins in the game tree.