Computing Abstraction Hierarchies by Numerical Simulation

No. 1: Agents, AI in Art and Entertainment, Knowledge Representation, and Learning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Knowledge Representation Abstraction

We present a novel method for building ABSTRIPS-style abstraction hierarchies in planning. The aim of this method is to minimize the amount of backtracking between abstraction levels. Previous approaches 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 demonstrate the theoretical advantages of our approach by identifying 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.