Inductive learning of search control rules for planning

摘要

One method for reducing the time required for plan generation is to learn search control rules from experience. The most common approach to learning search control knowledge has been explanationbased learning. An alternative approach is to use inductive learning. An inductive approach does not require a complete and tractable domain theory and has the potential to create more effective rules by learning from more than one example at a time. In this paper we describe Grasshopper, an inductive system that learns search control rules for a classical plan generation system. We also provide an empirical evaluation of Grasshopper by comparing it with an existing explanation-based learning system.