Artiﬁcial Intelligence Journal
We present a novel method for building style abstraction hierarchies in planning. The aim of this method is to minimize search by limiting backtracking both between abstraction levels and within an abstraction level. Previous approaches for building style abstractions have determined the criticality of operator preconditions by reasoning about plans directly. Here, we adopt a simpler and faster approach where we use numerical simulation of the planning process. We develop a simple but powerful theory to demonstrate the theoretical advantages of our approach. We use this theory to identify some simple properties lacking in previous approaches but possessed by our method. We demonstrate the empirical advantages of our approach by a set of four benchmark experiments using the system. We compare the quality of the abstraction hierarchies generated with those built by the and algorithms.