Learning-Assisted Automated Planning: Looking Back, Taking Stock, Going Forward
Learning-Assisted Automated Planning: Looking Back, Taking Stock, Going Forward
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学习辅助自动化规划：回顾、盘点、展望未来
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
S. Kambhampati
中科院分区：
文献类型：
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作者：
Terry L. Zimmerman;S. Kambhampati
本文报道了一项对过去30年中与机器学习应用于自动规划相关的研究工作的广泛调查和分析。主要研究贡献大致按学习方法分类，然后再细分描述性子类别。调查结果揭示了已被广泛应用的学习技术以及一些很少受到关注的技术。我们扩展了调查分析，根据规划领域以往的经验和当前的需求，为基于学习的未来研究提出了有前景的方向。
This article reports on an extensive survey and analysis of research work related to machine learning as it applies to automated planning over the past 30 years. Major research contributions are broadly characterized by learning method and then descriptive subcategories. Survey results reveal learning techniques that have extensively been applied and a number that have received scant attention. We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community.