BUILDING A TIC TAC TOE BOT IN PYTHON Kiran Gangadharan 

WHAT THIS TALK IS NOT ABOUT 

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BUILDING A SIMPLE, EFFECTIVE AI FOR TIC TAC TOE (that almost always beats you) 

MINIMAX 

MINIMAX ➤ A decision rule used in game theory to minimise the worst case scenario (loss). ➤ It’s about minimising one’s maximum loss or maximising one’s minimum gain (hence the term Minimax) ➤ Originally designed for two player games where each player would alternate turns. 

WHY USE MINIMAX? 

https://xkcd.com/832/ 

HOW DOES IT WORK? 

Image from http://neverstopbuilding.com/minimax 

ALGORITHM 

➤ If leaf node, return score from AI’s perspective ➤ Otherwise, get all possible moves in board ➤ For each move ➤ Add minimax of that move to a scores list ➤ If it’s the AI’s turn, return max(scores) ➤ If it’s the player’s turn, return min(scores) 

LET’S LOOK AT SOME CODE! 

MINIMAX def minimax(self, board, player): empty_cells = self.empty_cells(board) score = self.get_score(board) if score == 0: # Check for draw if len(empty_cells) == 0: return score else: return score scores = [] for cell in empty_cells: b = list(board) b[cell] = player scores.append(self.minimax(b, self.opponent(player))) if player == self.player: return max(scores) else: return min(scores) 

HEURISTIC FUNCTION def get_score(self, board): opp = self.opponent(self.player) win = self.has_won(board, self.player) has_two_in_line = self.has_two_in_line(board, self.player) opp_win = self.has_won(board, opp) opp_has_two_in_line = self.has_two_in_line(board, opp) score = 0 if win: score += 100 if has_two_in_line: score += 10 if opp_win: score -= 100 if opp_has_two_in_line: score -= 10 return score 

DEMO 

NEXT STEPS 

ALPHA-BETA PRUNING 

http://yuliana.lecturer.pens.ac.id/Kecerdasan%20Buatan/ppt/Game%20Playing/contoh%20prunning/ 

RESOURCES https://youtu.be/STjW3eH0Cik 

https://github.com/kirang89/tictac 

THANKS! 

kirang89 @kirang89 http://kirang.in