Learning-assisted automated planning: looking back, taking stock, going forward
Terry L. Zimmerman, Subbarao Kambhampati · 2003
This paper reports on an extensive survey and analysis of research work related to machine learning as applied to automated planning over the past 30 years. Major research contributions are characterized broadly by learning method and then into descriptive subcategories. Survey results reveal learning techniques that have been extensively 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