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EXPERIMENTS WITH LEARNING OPENING STRATEGY IN THE GAME OF GO
2004
International journal on artificial intelligence tools
We present an experimental methodology and results for a machine learning approach to learning opening strategy in the game of Go, a game for which the best computer programs play only at the level of an advanced beginning human player. While the evaluation function in most computer Go programs consists of a carefully crafted combination of pattern matchers, expert rules, and selective search, we employ a neural network trained by self-play using temporal difference learning. Our focus is on
doi:10.1142/s0218213004001430
fatcat:fhcj4kfq4fbvblgrkdhxduqgpa