Tic Tac Toe War Games: The Intelligent Minimax Algorithm

I have been wasting my time productively by writing games suh as the classic Pong in the R langage. In a previous post, I shared how to build a randomised Tic Tac Toe simulation. The computer plays against itself playing at random positions. In this post, I will share how to teach the computer to play the game strategically.

I love the 1983 classic movie War Games. In this film, a computer plays Tic Tac Toe against itself to learn that it cannot win the game to prevent a nuclear war.

Back in those days, I devoured the wonderful book Writing Strategy Games on your Atari by John White which contains an algorithm to play Tic Tac Toe War Games. This is my attempt to relive the eighties using R.

You can find the code on my GitHub page.

Drawing the Board

A previous post describes the function that draws the Tic Tac Toe board. For completeness, the code is replicated below. The game board is a vector of length nine consisting of either -1 (X), 0 (empty field) or 1 (O). The vector indices correspond with locations on the game board:

1 2 3
4 5 6
7 8 9

draw.board <- function(board) { # Draw the board
    xo <- c("X", " ", "O") # Symbols
    par(mar = rep(0,4))
    plot.window(xlim = c(0,30), ylim = c(0,30))
    abline(h = c(10, 20), col = "darkgrey", lwd = 4)
    abline(v = c(10, 20), col = "darkgrey", lwd = 4)
    pieces <- xo[board + 2]
    text(rep(c(5, 15, 25), 3), c(rep(25, 3), rep(15,3), rep(5, 3)), pieces, cex = 6)
    # Identify location of any three in a row
    square <- t(matrix(board, nrow = 3))
    hor <- abs(rowSums(square))
    if (any(hor == 3)) 
      hor <- (4 - which(hor == 3)) * 10 - 5 
      hor <- 0
    ver <- abs(colSums(square))
    if (any(ver == 3)) 
      ver <- which(ver == 3) * 10 - 5 
      ver <- 0
    diag1 <- sum(diag(square))
    diag2 <- sum(diag(t(apply(square, 2, rev)))) # Draw winning lines if (hor > 0) lines(c(0, 30), rep(hor, 2), lwd = 10, col = "red")
    if (ver > 0) lines(rep(ver, 2), c(0, 30), lwd = 10, col = "red")
    if (abs(diag1) == 3) lines(c(2, 28), c(28, 2), lwd = 10, col = "red")
    if (abs(diag2) == 3) lines(c(2, 28), c(2, 28), lwd = 10, col = "red")

Human Players

This second code snippet lets a human player move by clicking anywhere on the graphic display using the locator function. The click location is converted to a number to denote the position on the board. The entered field is only accepted if it has not yet been used (the empty variable contains the available fields).

move.human <- function(game) {
    text(4, 0, "Click on screen to move", col = "grey", cex=.7)
    empty <- which(game == 0)
    move <- 0
    while (!move %in% empty) {
        coords <- locator(n = 1) # add lines
        coords$x <- floor(abs(coords$x) / 10) + 1
        coords$y <- floor(abs(coords$y) / 10) + 1
        move <- coords$x + 3 * (3 - coords$y)
    return (move)

Evaluate the Game

This code snippet defines the eval.game function which assesses the current board and assigns a score. Zero means no outcome, -6 means that the X player has won and +6 implies that the O player has won.

eval.game <- function(game, player) {
    # Determine game score
    square <- t(matrix(game, nrow = 3))
    hor <- rowSums(square)
    ver <- colSums(square)
    diag1 <- sum(diag(square))
    diag2 <- sum(diag(t(apply(square, 2, rev))))
    eval <- c(hor, ver, diag1, diag2)
    # Determine best score
    minimax <- ifelse(player == -1, "min", "max")
    best.score <- do.call(minimax, list(eval))
    if (abs(best.score) == 3) best.score <- best.score * 2
    return (best.score)

Computer Moves

The computer uses a modified Minimax Algorithm to determine its next move. This article from the Never Stop Building blog and the video below explain this method in great detail.

The next function determines the computer’s move. I have not used a brute-force minimax algorithm to save running time. I struggled to build a fully recursive minimax function. Perhaps somebody can help me with this. This code looks only two steps deep and contains a strategic rule to maximise the score.

The first line stores the value of the players move, the second remainder of the matrix holds the evaluations of all the opponent’s moves. The code adds a randomised variable, based on the strategic value of a field. The centre has the highest value because it is part of four winning lines. Corners have three winning lines and the rest only two winning lines. This means that the computer will, all things being equal, favour the centre over the corners and favour the other fields least. The randomised variables in the code ensure that the computer does not always pick the same field in a similar situation.

move.computer <- function(game, player) {
    empty <- which(game == 0)
    eval <- matrix(nrow = 10, ncol = 9, data = 0)
    for (i in empty) {
        game.tmp <- game
        game.tmp[i] <- player
        eval[1, i] <- eval.game(game.tmp, player)
        empty.tmp <- which(game.tmp ==0)
        for (j in empty.tmp) {
            game.tmp1 <- game.tmp
            game.tmp1[j] <- -player
            eval[(j + 1), i] <- eval.game(game.tmp1, -player)
    if (!any(abs(eval[1,]) == 6)) { # When winning, play move
        # Analyse opponent move
        minimax &<- ifelse(player == -1, "max", "min") # Minimax
        best.opponent <- apply(eval[-1,], 1, minimax)
        eval[1,] <- eval[1,] * -player * best.opponent
    # Add randomisation and strategic values
    board <- c(3, 2, 3, 2, 4, 2, 3, 2, 3) # Strategic values
    board <- sapply(board, function(x) runif(1, 0.1 * x, (0.1 * x) + 0.1)) # Randomise
    eval[1, empty] <- eval[1, empty] + player * board[empty] # Randomise moves
    # Pick best game
    minimax <- ifelse(player == -1, "which.min", "which.max") # Minimax
    move <- do.call(minimax, list(eval[1,])) # Select best move

This last code snippet enables computers and humans play each other or themselves. The players vector contains the identity of the two players so that a human can play a computer or vice versa. The human player moves by clicking on the screen. The loop keeps running until the board is full or a winner has been identified. A previous Tic Tac Toe post explains the draw.board function.

tic.tac.toe <- function(player1 = "human", player2 = "computer") {
    game <- rep(0, 9) # Empty board
    winner <- FALSE # Define winner
    player <- 1 # First player
    players <- c(player1, player2)
    while (0 %in% game & !winner) { # Keep playing until win or full board
        if (players[(player + 3) %% 3] == "human") # Human player
            move <- move.human(game)
        else # Computer player
            move <- move.computer(game, player)
        game[move] <- player # Change board
        winner <- max(eval.game(game, 1), abs(eval.game(game, -1))) == 6 # Winner, winner, chicken dinner?
        player <- -player # Change player

You can play the computer by running all functions and then entering tic.tac.toe(). I am pretty certain this simplified minimax algorithm is unbeatable—why don’t you try to win and let me know when you do.

Tic Tac Toe War Games

Now that this problem is solved, I can finally recreate the epic scene from the WarGames movie. The Tic Tac Toe War Games code uses the functions explained above and the animation package. Unfortunately, there are not many opportunities to create sound in R.

5 thoughts on “Tic Tac Toe War Games: The Intelligent Minimax Algorithm

  1. There is a way to beat the algorithm. Given I have the first move, place it on one of the corners. The algorithm will chose the center since it has the highers score on “board”. Then chose the opposite corner to the one I chose on my first move. The algorithm will chose one of the two available corners since they have higher scores on “board” after the center. Place my third move on the last available corner. I have won the game by this point since I have two potential winning moves and the other player can only block one.

    I win because the algorithm only looks two steps ahead and it needs to look three to figure out my strategy. Given that this might be the only way to fool the algorithm, we could solve it with brute force (i.e. feeding it an if statement that changes the values on “board” whenever I have placed my first two moves on opposite corners) or by teaching it to look three steps ahead (this is considerably harder). I am currently working on implementing three steps; however, there are bugs in the algorithm and at the moment I am too tired to find them.

    I loved the article by the way. It taught me a lot. I hope to be able to learn from cool projects of yours in the future.

    1. Bummer, I thought I had fixed that. I let the computer play itself lots but this did not come up.

      I struggled writing a proper minimax algorithm – one that reviews all moves. If somebody can help me with this that would be great.

      Back to the drawing board. Thanks for finding the bug.

    2. I started writing code that generates and evaluates all possible moves:

      game.tree <- function(game, player, depth = 3, d.count = 0) {
          d.count <- d.count + 1
          for (i in which(game == 0)) {
              game.tmp <- game
              game.tmp[i] <- player
              score <- eval.game(game.tmp, player)
              print(paste(paste(game.tmp, collapse = ","), "|", score, "|", d.count))
              text(15,15, score, col = "lightblue", cex=10)
              if (d.count < depth) Recall(game.tmp, -player, depth, d.count)
      game.tree(game, player = 1)

      Next step will be to work out how to implement MiniMax to find the best move to make it truly unbeatable.

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