Learning Search Control Rules for Planning: An Inductive Approach

The computational complexity of planning has motivated significant efforts in machine learning. However, much of this work has concentrated on explanation-based learning techniques. An alternative approach is to use inductive learning. An inductive approach does not require a complete and tractable domain theory to be encoded and has the potential to create more effective rules by learning from more than one example at a time. In this paper, we describe an inductive system for learning search control rules and compare it with an existing explanation-based learning system.