Finding the Best Move :
We shall be introducing a new function called findBestMove(). This function evaluates all the available
moves using minimax() and then returns the best move the maximizer can make. The pseudocode is as
follows :
function findBestMove(board):
bestMove = NULL
for each move in board :
if current move is better than bestMove
bestMove = current move
return bestMove
Minimax :
To check whether or not the current move is better than the best move we take the help of minimax()
function which will consider all the possible ways the game can go and returns the best value for that move,
assuming the opponent also plays optimally
The code for the maximizer and minimizer in the minimax() function is similar to findBestMove() , the
only difference is, instead of returning a move, it will return a value. Here is the pseudocode :
function minimax(board, depth, isMaximizingPlayer):
if current board state is a terminal state :
return value of the board
if isMaximizingPlayer :
bestVal = -INFINITY
for each move in board :
value = minimax(board, depth+1, false)
bestVal = max( bestVal, value)
return bestVal
else :
bestVal = +INFINITY
for each move in board :
value = minimax(board, depth+1, true)
bestVal = min( bestVal, value)
return bestVal
Checking for GameOver state :
To check whether the game is over and to make sure there are no moves left we use isMovesLeft() function.
It is a simple straightforward function which checks whether a move is available or not and returns true or
false respectively. Pseudocode is as follows :
function isMovesLeft(board):
for each cell in board:
if current cell is empty:
return true
return false
Making our AI smarter :
One final step is to make our AI a little bit smarter. Even though the following AI plays perfectly, it might
choose to make a move which will result in a slower victory or a faster loss. Lets take an example and
explain it.
Assume that there are 2 possible ways for X to win the game from a give board state.
•Move A : X can win in 2 move
•Move B : X can win in 4 moves
Our evaluation function will return a value of +10 for both moves A and B. Even though the move A is
better because it ensures a faster victory, our AI may choose B sometimes. To overcome this problem we
