We discuss learning and the adaptive generation of concrete strategies through interactive experience. The domain is the game Tictactoe. The knowledge structures embodying strategies we represent as having tree parts: a Goal, a sequence of Actions, and a set of Constraints on those actions (GAC). We simulate such structures in a program that plays Tictactoe against different kinds of opponents. Applying these strategies leads to moves that often result in winning or losing; which in turn leads to the creation of new structures, by modifying the current GACs. These modifications are controlled by a small set of specific rules, so that the GACs are related by the ways modifications can map from one to another. Subject to certain limitations, we do a complete exploration of certain classes of strategy. This learnability analysis takes guidance from previous cognitive studies of a human subject by Lawler. The simulations were performed on a Symbolics 3600 in LISP. This work avoids abstractions in order to explore learning