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Toward an Ideal Trainer

Toward an Ideal Trainer,10.1007/BF00993346,Machine Learning,Susan L. Epstein,Steve Minton

Toward an Ideal Trainer   (Citations: 32)
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This paper demonstrates how the nature of the opposition during training affects learning to play two-person, perfect information board games. It considers different kinds of competitive training, the impact of trainer error, appropriate metrics for post-training performance measurement, and the ways those metrics can be applied. The results suggest that teaching a program by leading it repeatedly through the same restricted paths, albeit high quality ones, is overly narrow preparation for the variations that appear in real-world experience. The results also demonstrate that variety introduced into training by random choice is unreliable preparation, and that a program that directs its own training may overlook important situations. The results argue for a broad variety of training experience with play at many levels. This variety may either be inherent in the game or introduced deliberately into the training. Lesson and practice training, a blend of expert guidance and knowledge-based, self-directed elaboration, is shown to be particularly effective for learning during competition.
Journal: Machine Learning - ML , vol. 15, no. 3, pp. 251-277, 1994
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