What Next for Learning in AI Planning?

摘要

This paper reports on a comprehensive survey of research work related to machine learning as applied to AI planning over the past 15 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.