MittelZielAnalyse

MittelZielAnalyse, also known as means-ends analysis, is a problem-solving heuristic prominent in artificial intelligence and cognitive psychology. It provides a framework for planning sequences of actions by focusing on reducing the difference between the current state and a desired goal state. The approach was developed by Allen Newell and Herbert A. Simon as part of symbolic problem-solving research and featured prominently in early AI planning and expert-system work.

The method proceeds by comparing the current state to the goal, selecting a goal-reducing difference, and identifying an operator (action) that can bridge that gap. If an applicable operator exists, it is applied and any new subgoals created as a result are solved recursively. If no operator directly reduces the difference, the analyst or the system backtracks to try alternative subgoals or operators, hoping to find a feasible path to the goal.

Applications include general problem solving, rule-based planning, strategic game playing, robotics, and educational simulations. It has influenced AI planning techniques and has been used to model human problem solving, though in practice many modern planners combine subgoal decomposition with heuristic search and constraint satisfaction.

Limitations include sensitivity to the definition of states and operators, potential combinatorial explosion, and difficulties in continuous or uncertain environments. The technique remains a foundational concept in understanding how agents structure goals and select actions to reach them.

See also: Means-Ends Analysis; AI planning; problem solving; heuristic search.