We present a novel method for building ABSTRIPS 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 ABSTRIPS 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 ABTWEAK system. We compare the quality of the abstraction hierarchies generated with those built by the ALPINE and HIGHPOINT algorithms.
Bundy, A., Giunchiglia, F., Sebastiani, R., & Walsh, T. (2002). Calculating criticalities. Artificial Intelligence, 88(1–2), 39–67. https://doi.org/10.1016/s0004-3702(96)00019-7