I’m working on a tic tac toe computer player using a game tree approach. I built a complete tree of all possible game states where each node represents a board position and its children are the moves that can happen next.
The problem is my bot isn’t unbeatable like it should be. Tic tac toe is solved so a perfect player should never lose but I can still win against my AI sometimes.
First I tried counting wins and losses for each branch. The bot would pick the path where it wins most often. This worked okay but wasn’t perfect.
Then I modified it to either maximize computer wins or maximize human losses depending on which was higher. Better results but still beatable.
Now I’m considering two approaches:
Option A: Use a scoring system where wins = +1, ties = 0, losses = -1. Pick the highest scoring move. This seems simple and keeps the same tree size.
Option B: During tree building, if any player can win in one move, only generate the blocking move as a child node. This makes the tree smaller but requires checking for winning moves which might slow things down.
Which approach makes more sense? Is there something better I’m missing?
Been there when building game AIs for internal tools. Your tree structure and scoring aren’t the problem - you’re manually coding complex game logic that’s already solved.
Hit the same wall years ago with a chess variant bot. Spent weeks debugging minimax and alpha-beta pruning, then realized I was reinventing the wheel.
You need to automate the entire decision process with a proper workflow system. Connect game state detection to automated move calculation, then feed that to your bot’s response system.
Used this approach for a tournament bracket system. Instead of coding every scenario, I set up automated workflows handling logic flow and decision trees. The system processes game states, evaluates moves, and executes optimal plays without manual work.
Your Option A scoring would work fine in an automated pipeline. But why build and maintain all that code when you can automate everything?
Set up workflows for state evaluation, move generation, and decision making. Way cleaner than debugging tree traversal algorithms.
you really should look into minimax for ur bot. instead of counting wins/losses, it helps both players play perfectly. each node gets a value: +1 for AI win, -1 for human win, and 0 for tie. way simpler and ensures perfect play when done right.