On Measuring the Impact of Human Actions in the Machine Learning of a Board Game's Playing Policies

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We investigate systematically the impact of human intervention in the training of computer players in a strategy board game. In that game, computer players utilise reinforcement learning with neural networks for evolving their playing strategies and demonstrate a slow learning speed. Human intervention can significantly enhance learning performance, but carry-ing it out systematically seems to be more of a problem of an integrated game development environment as opposed to automatic evolutionary learning.
Contains 19 pages, 10 figures, 8 tables. Submitted to a journal

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Artificial Intelligence, Computer Science and Game Theory, Neural and Evolutionary Computing

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http://salesiana.dossiersoluciones.com/handle/123456789/118885

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/0611163
 http://arxiv.org/abs/cs/0611163

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arXiv