Using Generic Preferences to Incrementally Improve Plan Quality

机译：使用通用首选项逐步提高计划质量

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We describe a methodology for representing and optimizing user preferences on plans. Our approach differs from previous work on plan optimization in that we employ a generalization of commonly occurring plan quality metrics, providing an expressive preference language. We introduce a domain independent algorithm for incrementally improving the quality of feasible plans with respect to preferences described in this language. Finally, we experimentally show that plan quality can be significantly increased with little additional modeling effort for each domain.

机译：我们描述了一种用于表示和优化计划中用户偏好的方法。我们的方法与之前有关计划优化的工作不同，因为我们采用了常见计划质量指标的概括，提供了表达性偏好语言。我们介绍了一种与域无关的算法，用于相对于使用此语言描述的偏好来逐步提高可行计划的质量。最后，我们通过实验表明，对于每个领域，几乎不需要任何额外的建模工作就可以显着提高计划质量。

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来源
《International Conference on Artificial Intelligence Planning and Scheduling; 2000414-17; Breckenridge,CO(US)》|2000年|P.236-245|共10页
会议地点 BreckenridgeCO(US)
作者
Gregg Rabideau; Barbara Engelhardt; Steve Chien;
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Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive, M/S 126-347 Pasadena, California 91109-8099;

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原文格式 PDF
正文语种 eng
中图分类人工智能理论;
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Using Generic Preferences to Incrementally Improve Plan Quality

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