How does a computer play a game too big to solve? It doesn’t — it GUESSES by playing thousands of random games from each move and favouring the ones that win more, while still occasionally trying neglected moves in case they’re secretly good. That balance of exploit-and-explore, formalized as UCB1, is the heart of the search that beat the world at Go. Slide to run more playouts and watch the best move rise.
Monte Carlo Tree Search grows a search tree by four steps — SELECT (descend by UCB1), EXPAND, SIMULATE (a random rollout), BACKPROPAGATE (update win counts). UCB1 picks the child maximizing wᵢ/nᵢ + c·√(ln N / nᵢ): the first term EXPLOITS the current best average, the second EXPLORES rarely-tried moves (it’s large when nᵢ is small). As playouts grow, estimates converge on the strong moves. It powered AlphaGo. A fail-loud self-check throws unless a rarely-visited arm gets a higher UCB1 bonus than a well-visited one at equal mean. ◆ real game AI, node-verified.
The rollouts here are a fixed illustrative model, not a live game engine; the UCB1 selection rule and the convergence-with-playouts behaviour are the real content. Real strength also needs a good simulation/](or neural) policy, not just the tree.