FIELD NOTES / 01 SEARCH SYSTEMS LAB

INTERACTIVE EXPLORERMONTE CARLO TREE SEARCH

Think ahead.
Then think again.

Watch a search algorithm turn uncertainty into a move. Play Tic-Tac-Toe as X, then open the tree and see where O spent its simulations.

⌘ No model requiredRuns locally in your browser

01 — PLAY THE GAME

Tic-Tac-Toe

Your move
X YOUO MCTS AGENT
↗
Your turn

Choose any open square to begin.

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THE IDEA

Search by
sampling futures.

MCTS does not need a handcrafted strategy or a trained model. It grows a decision tree by repeatedly exploring possible continuations, simulating outcomes, and using those outcomes to focus the next search.

UCB1 SELECTION SCOREwin rate + C × √(ln(parent visits) / child visits)Balance reward against uncertainty.