@inproceedings{09595a85f4254202b0f798d2c72afcbb,
title = "Framework of simplifications in learning to plan",
abstract = "Learning shows great promise to extend the generality and effectiveness of planning techniques. Research in this area has generated an impressive battery of techniques and a growing body of empirical successes. Unfortunately the formal properties of these systems are not well understood. This is highlighted by a growing corpus of demonstrations where learning actually degrades planning performance. In this paper we view learning to plan as a search problem. We argue that the complexity of this search precludes a general solution and can only be approached by making simplifying assumptions. We discuss the frequently unarticulated commitments which underly current learning approaches. From these we assemble a framework of simplifications which a learning planner can draw upon. These simplifications improve learning efficiency but not without tradeoffs.",
author = "Jonathan Gratch and Gerald DeJong",
year = "1992",
language = "English (US)",
isbn = "155860250X",
series = "Proc 1 Int Conf Artif Intell Plann Syst",
publisher = "Publ by Morgan Kaufmann Publ Inc",
pages = "78--87",
booktitle = "Proc 1 Int Conf Artif Intell Plann Syst",
note = "Proceedings of the 1st International Conference on Artificial Intelligence Planning Systems ; Conference date: 15-06-1992 Through 17-06-1992",
}