# Why does the experience propagation rule for checkers work in Tom Mitchell's book?

In Tom Mitchell's book "Machine Learning", Chap.1, a checkers game is used to illustrate how machine learning can be applied solve problems.

An experience propagation rule is described for iterative learning of a hypothesis. Suppose a game has been played and watched by the program, the state of the endgame is labeled 100 for winning and -100 for losing. For each of the states on the path toward the endgame, we label it as $\hat{V}(Successor)$, where $\hat{V}(state)$ is the current model output on some state. Then the model is trained by adding the new label, and iteratively, the model converges to a good checkers program.

Why does this experience propagation rule work? It is mentioned in the book that it works quite well for most chess games.

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