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

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

2006-11-30

arxiv.org/abs/cs/0611163v1

Computer Science

Artificial Intelligence

Contains 19 pages, 10 figures, 8 tables. Submitted to a journal

Scientific paper

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.

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