As described here, pragmatic navigation attempts to harness simp.le facts about a two-dimensional environment to facltitate travel through it without an explicit map. It reties upon predefmed spatial representations whose explicit instances are learned during a sequence of trips through a fixed maze. Once learned, any of these instances can be applied to subsequent travel. Some of the representations are heuristic, as are the procedures that employ them. The resultant performance of an implementation, particularly when contrasted with traditional AI techniques, argues for pathfinding guided by representations like those detailed here.