*download the project repository(folder) and extract it.
*open the project in pycharm
*install pygames library in pycharm
*open terminal in pycharm and run : "python runner.py"
*and rest will be explained by the GUI itself...
Minimax
A type of algorithm in adversarial search, Minimax represents winning conditions as (-1) for one side and (+1) for the other side. Further actions will be driven by these conditions, with the minimizing side trying to get the lowest score, and the maximizer trying to get the highest score.
Representing a Tic-Tac-Toe AI:
S₀: Initial state (in our case, an empty 3X3 board)
Players(s): a function that, given a state s, returns which player’s turn it is (X or O).
Actions(s): a function that, given a state s, return all the legal moves in this state (what spots are free on the board).
Result(s, a): a function that, given a state s and action a, returns a new state. This is the board that resulted from performing the action a on state s (making a move in the game).
Terminal(s): a function that, given a state s, checks whether this is the last step in the game, i.e. if someone won or there is a tie. Returns True if the game has ended, False otherwise.
Utility(s): a function that, given a terminal state s, returns the utility value of the state: -1, 0, or 1.
How the algorithm works:
Recursively, the algorithm simulates all possible games that can take place beginning at the current state and until a terminal state is reached. Each terminal state is valued as either (-1), 0, or (+1).
Minimax Algorithm in Tic Tac Toe
Knowing based on the state whose turn it is, the algorithm can know whether the current player, when playing optimally, will pick the action that leads to a state with a lower or a higher value. This way, alternating between minimizing and maximizing, the algorithm creates values for the state that would result from each possible action. To give a more concrete example, we can imagine that the maximizing player asks at every turn: “if I take this action, a new state will result. If the minimizing player plays optimally, what action can that player take to bring to the lowest value?” However, to answer this question, the maximizing player has to ask: “To know what the minimizing player will do, I need to simulate the same process in the minimizer’s mind: the minimizing player will try to ask: ‘if I take this action, what action can the maximizing player take to bring to the highest value?’” This is a recursive process, and it could be hard to wrap your head around it; looking at the pseudo code below can help. Eventually, through this recursive reasoning process, the maximizing player generates values for each state that could result from all the possible actions at the current state. After having these values, the maximizing player chooses the highest one.