Abstract Planning with Unknown Object Quantities and Properties

State abstraction has been widely used for state aggregation in approaches to AI search and planning. In this paper we use a powerful abstraction technique from software model checking for representing collections of states with different object quantities and properties. We exploit this method to develop precise abstractions and action operators for use in AI. This enables us to find scalable, algorithm-like plans with branches and loops which can solve problems of unbounded sizes. We describe how this method of abstraction can be effectively used in AI, with compelling results from implementations of two planning algorithms.