The effective reuse of domain theories in problem solving requires the problem-solving agent to identify general theories whose properties "scale up": they hold for a class of problems of varying size. Otherwise, the agent will be overwhelmed by the cost of indexing and retrieving a huge collection of domain theories, each of which applies in very restricted cases. Furthermore, these general theories need to be represented in a manner that is as independent as possible of the circumstances of particular cases. This paper describes research on analysis and reformulation of domain theories. The perspective of this work is to view a problem space as though it were physical space, and the actions in the problem space as though they were physical motions. A domain theory should then state the laws of motion within the space. Following the analogy with physics, a representation is a coordinate system, and theories are reformulated by transforming coordinates. The mathematical basis for this analogy is briefly given, and illustrated on two simple examples.