meansendanalys

Means-end analysis is a problem-solving method used in cognitive psychology and artificial intelligence. The approach aims to reduce the difference between the current state and a desired goal by selecting actions, called means or operators, that transform the state toward the goal. The method relies on subgoals: large gaps are broken down into smaller tasks that are easier to manage, so progress is made in a stepwise fashion.

Origin and use: The technique was developed by Allen Newell and Herbert Simon as part of the General Problem Solver framework in the late 1950s. It became a central concept for modeling human problem solving and for early AI planning, where a solver chooses actions that progressively diminish the discrepancy between the current and the goal state.

Procedure: Given a current state and a goal, the solver identifies the most relevant difference, selects a means (an operator) that can reduce that difference, and applies it to produce a new state. If new subgoals are revealed, the process repeats recursively. If no operator can reduce the difference, backtracking or attempting alternative operators is used.

Applications and examples: It is used to study how people plan and solve tasks, and in AI systems for planning and automated reasoning. In practice, means-end analysis underpins hierarchical problem solving, formal planning, and various instructional simulations that demonstrate strategic thinking.

Limitations: The approach assumes an explicit state and action space and can suffer from combinatorial explosion in complex domains. Real-world problems often require additional heuristics, learning, or probabilistic reasoning. Critics note that purely means-end strategies can overlook alternative approaches and depend on strong domain knowledge.