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

Terry Zimmerman, Subbarao Kambhampati

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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DOI: http://dx.doi.org/10.1609/aimag.v24i2.1705

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