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  • 1
    Electronic Resource
    Electronic Resource
    Toward an Ideal Trainer (1994)
    Springer
    Machine learning 15 (1994), S. 251-277 
    Details
    ISSN: 0885-6125
    Keywords: training ; competition ; game playing ; reliability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022637925775
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Toward an ideal trainer (1994)
    Springer
    Machine learning 15 (1994), S. 251-277 
    Details
    ISSN: 0885-6125
    Keywords: training ; competition ; game playing ; reliability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993346
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    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    The role of memory and concepts in learning (1992)
    Springer
    Minds and machines 2 (1992), S. 239-265 
    Details
    ISSN: 1572-8641
    Keywords: Representation ; cognitive architecture ; concepts ; machine learning ; game playing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract The extent to which concepts, memory, and planning are necessary to the simulation of intelligent behavior is a fundamental philosophical issue in Artificial Intelligence. An active and productive segement of the AI community has taken the position that multiple low-level agents, properly organized, can account for high-level behavior. Empirical research on these questions with fully operational systems has been restricted to mobile robots that do simple tasks. This paper recounts experiments with Hoyle, a system in a cerebral, rather than a physical, domain. The program learns to perform well and quickly, often outpacing its human creators at two-person, perfect information board games. Hoyle demonstrates that a surprising amount of intelligent behavior can be treated as if it were situation-determined, that often planning is unnecessary, and that the memory required to support this learning is minimal. Concepts, however, are crucial to this reactive program's ability to learn and perform.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02454222
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    Paper (German National Licenses)
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