Learning-Assisted Automated Planning: Looking Back, Taking Stock, Going Forward

Authors

  • Terry Zimmerman
  • Subbarao Kambhampati

DOI:

https://doi.org/10.1609/aimag.v24i2.1705

Abstract

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.

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Published

2003-06-15

How to Cite

Zimmerman, T., & Kambhampati, S. (2003). Learning-Assisted Automated Planning: Looking Back, Taking Stock, Going Forward. AI Magazine, 24(2), 73. https://doi.org/10.1609/aimag.v24i2.1705

Issue

Section

Articles