In the summer of 1957, during a workshop at Carnegie Tech on organizational behavior, Al and I extracted from the protocol of a single subject solving logic problems what proved to be a key mechanism in human problem solving: means-ends analysis. In M-E analysis the problem solver compares the current situation with the goal situation; finds a difference between them; finds in memory an operator that experience has taught reduces differences of this kind; and applies the operator to change the situation. Repeating this process the goal may gradually be attained, although there are generally no guarantees that the process will succeed. The idea of M-E analysis led to the General Problem Solver (Newell, Shaw, and Simon, 1960), a program that could solve problems in a number of domains after being provided with a problem space (domain representation), operators to move through the space, and information about which operators were relevant for reducing which differences. The research also discovered schemes that permitted GPS to produce its own operators from a small set of primitives and to learn which operators were relevant for reducing which differences.

The IPL languages in artificial intelligence and their contemporary FORTRAN in numerical computing settled once and for all the essentiality of higher-level languages for sophisticated programming. The IPLs were designed to meet the needs for flexibility and generality: flexibility, because it is impossible in these kinds of computations to anticipate before run time what sorts of data structures will be needed and what memory allocations will be required for them; generality, because the goal is not to construct programs