This paper presents a method for abstracting action representation, reducing the computational burden of stochastic planning. Previous methods of forming abstractions in probabilistic environments have relied on summarizing clusters of states or actions with worst-case or average feature values. In contrast to these, the current proposal treats abstract actions as plans to plan. An underspecified action sequence is used in abstract plans like the expected consequences of its realization. This more accurately reflects our natural use of abstract actions, and improves their utility for planning. An exemplification of this idea is presented for maze route-finding modeled as a Markov decision process.