Means-end analysis is one of the most general and powerful heuristic strategies for problem solving, identified by Newell and Simon as central to human problem-solving behavior. The strategy works by: (1) comparing the current state to the goal state and identifying the most important difference, (2) finding an operator (action) that reduces this difference, and (3) if the operator cannot be applied directly, setting up a subgoal to enable its application. This recursive process of setting and achieving subgoals can solve complex problems by breaking them into manageable steps.
The General Problem Solver
Newell and Simon implemented means-end analysis in their General Problem Solver (GPS, 1957) — one of the earliest artificial intelligence programs. GPS could solve logical proofs, play chess endgames, and solve the Tower of Hanoi puzzle using the same general strategy. While GPS was too weak to handle complex real-world problems (which require extensive domain-specific knowledge), it demonstrated that a single problem-solving strategy could apply across diverse domains.
Means-end analysis can fail when the solution requires temporarily moving away from the goal (increasing the current-goal difference before reducing it). The Tower of Hanoi puzzle requires such counterintuitive moves, and problems requiring them are consistently harder for human solvers. This limitation reflects a general bias in human problem solving: we prefer actions that appear to make immediate progress toward the goal, even when the globally optimal strategy requires temporary regression.