On Measuring the Impact of Human Actions in the Machine Learning of a Board Game's Playing Policies
<|reference_start|>On Measuring the Impact of Human Actions in the Machine Learning of a Board Game's Playing Policies: 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.<|reference_end|>
@article{kalles2006on,
    title={On Measuring the Impact of Human Actions in the Machine Learning of a
  Board Game's Playing Policies},
    author={Dimitris Kalles (Hellenic Open University)},
    journal={arXiv preprint arXiv:cs/0611163},
    year={2006},
    archivePrefix={arXiv},
    eprint={cs/0611163},
    primaryClass={cs.AI cs.GT cs.NE}
}