Meansends

Meansends, or means-ends analysis, is a problem-solving approach that guides action by continually reducing the difference between the current state and a desired goal. An agent starts with a goal state, assesses the current state, and selects actions whose effects bring the situation closer to the goal. When the gap is too large for a single action to close, the agent decomposes the goal into subgoals and solves them recursively until primitive actions are available.

The method was developed in artificial intelligence and cognitive psychology as a general planning strategy. It was formalized in the work of Allen Newell and Herbert A. Simon and played a central role in the General Problem Solver and early planning systems. In practice, means-ends analysis is often implemented with heuristics and domain knowledge that identify operators likely to reduce the distance to the goal, rather than relying on blind trial-and-error. The approach influenced subsequent planning paradigms, including heuristic search, hierarchical planning, and STRIPS-style planners.

Strengths of means-ends analysis include its goal-directed focus and its ability to structure complex problems into manageable subproblems. Its main weaknesses involve brittleness in dynamic or poorly specified domains, potential for getting trapped in local optima or cycles, and the need for a suitable set of operators and subgoals. Modern AI typically combines means-ends reasoning with more scalable planning techniques, constraint handling, and learning-based components to handle large, real-world task spaces.

Beyond AI, the term also appears in philosophy and ethics as instrumental reasoning about actions to achieve ends, though in technical contexts it is closely tied to planning and problem solving.