fishdan

Understanding AI via Tic-Tac-Toe

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by

dan

Most people think “AI” means neural networks, training data, and a lot of math. Tic-Tac-Toe is a great counterexample.

You can build a perfect Tic-Tac-Toe AI without learning, randomness, or guesswork—just logic, structure, and a clear way of evaluating outcomes.

This post explains how an AI Tic-Tac-Toe engine works conceptually, without code, using plain language.

The Core Idea

At its heart, Tic-Tac-Toe is a solved game.

That means:

  • Every possible board position has a known outcome
  • With perfect play, the result is always predictable

So the goal of our AI is simple:

Given a board, choose the move that leads to the best possible outcome.

To do that, we assign scores to game states:

  • +1 → the current player can force a win
  • 0 → the best possible result is a draw
  • −1 → the current player will lose if the opponent plays optimally

The AI doesn’t “think” — it looks up outcomes and chooses the best one.

Step 1: Represent the Board

Every Tic-Tac-Toe board can be represented in a structured way, for example:

  • A 3×3 grid
  • Each cell is:
    • X
    • O
    • Empty
  • Plus: whose turn it is

This board representation becomes a key in a data structure.

Step 2: Define Terminal States

Some boards are already finished. These are called terminal states.

Examples:

  • X has three in a row → win
  • O has three in a row → loss
  • Board is full with no winner → draw

These boards are easy to score.

  • Win → +1
  • Draw → 0
  • Loss → −1

These are the leaves of the game tree.

Step 3: Build the Game Tree

From any non-terminal board:

  1. List all legal moves
  2. Apply each move to create a new board
  3. Repeat until you reach terminal states

This forms a tree of all possible games, branching at every move.

For Tic-Tac-Toe, this tree is small enough to build completely.

Step 4: Work Backwards (Backward Induction)

Now comes the key idea.

You don’t assign values going forward — you assign them backwards.

For each non-terminal board:

  • Look at the scores of all possible next boards
  • Assume:
    • You play optimally
    • Your opponent plays optimally

Then:

  • If it’s your turn, you choose the move with the highest score
  • If it’s your opponent’s turn, assume they choose the lowest score

This logic propagates upward until every possible board has a score.

This process is often called minimax, but it’s really just rational decision-making under opposition.

Step 5: Store the Results

At the end, you have a simple mapping:

Board State → Best Possible Outcome

This can be stored as:

  • A map
  • A table
  • A dictionary
  • Any structure that lets you quickly look up a board

Once this exists, the hard work is done.

Step 6: Playing the Game

When the AI is playing it doesn’t care who’s REALLY X or O. It chooses the best move REGARDLESS of whose turn it is. It looks at the board and if an even number of moves have been made, it knows that it is X’s turn, and produces the best possible move. If an odd number of moves have been made, in it’s “mind” if flips all the X and Os, finds the best move for X, and then marks an O in that position.

So the steps in order:

  1. Look at the current board
  2. Generate all legal moves
  3. Look up the score for each resulting board
  4. Choose the move with the highest score.

No search.
No evaluation heuristics.
No randomness.

Just lookup and comparison.

Why This Works for Tic-Tac-Toe

Tic-Tac-Toe has:

  • A small state space
  • A finite number of legal boards
  • Perfect information
  • No hidden randomness

That makes it ideal for exhaustive analysis.

Larger games like Chess or Go explode in complexity and can’t be solved this way — but the underlying idea still matters.

Closing: Why This Matters Beyond Tic-Tac-Toe

What you’ve built here is more than a game engine.

You’ve created:

  • A value function (board → outcome)
  • A policy (choose the move with the best value)
  • A complete example of backward reasoning

This exact structure shows up again in modern AI — just scaled up.

A neural network is essentially:

  • A lossy, compressed approximation of a massive lookup table like this
  • Using continuous values instead of exact scores
  • Trading perfection for scalability

Tic-Tac-Toe lets you see the idea clearly, without the noise.

If you understand this engine, you already understand the foundation of game-playing AI.

And that’s a stronger position than most people realize.

Perfect play doesn’t require learning — it requires structure.

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