Means–ends analysis

Means–ends analysis (MEA) is a problem solving technique used in artificial intelligence (AI) for limiting search in AI algorithms. It is a method of directing the search in problem spaces towards the goal from the current position by identifying the "means" necessary to reach an "ends" or goal. This approach is particularly useful in solving complex problems where the path to the goal is not immediately obvious. Means–ends analysis is a core component of many AI systems, including those designed for decision making, planning, and automated reasoning.

Overview

Means–ends analysis operates by comparing the current state to the goal state and determining the most significant difference between the two. Once this difference is identified, the system searches for actions (means) that can reduce or eliminate this difference. The process is recursive and continues until the goal state is achieved or no further actions can be identified to reduce the difference between the current state and the goal state.

The technique was first described by Allen Newell and Herbert A. Simon in their work on the General Problem Solver (GPS), an early AI program designed to mimic human problem-solving behavior. GPS, and by extension means–ends analysis, was foundational in the development of AI as a field, illustrating how computers could be programmed to simulate complex cognitive processes.

Application

Means–ends analysis has been applied in various domains within artificial intelligence, including:

• Expert systems: MEA is used to guide the inference engine of expert systems towards the solution of complex problems by breaking them down into more manageable sub-problems.
• Robotics: In robotics, MEA helps in planning and executing tasks by identifying the steps needed to move from an initial state to a desired goal state.
• Game AI: In game AI, means–ends analysis is used to devise strategies and make decisions that bring the game entity closer to winning or achieving the game's objectives.

Advantages and Limitations

The primary advantage of means–ends analysis is its ability to efficiently navigate large problem spaces by focusing on actions that directly contribute to achieving the goal. This makes it particularly useful in situations where the path to the goal is not linear or obvious.

However, the effectiveness of MEA is heavily dependent on the ability to accurately identify the differences between the current state and the goal state and to have a comprehensive set of actions available to address these differences. In complex or poorly understood domains, this can be a significant limitation.

See Also

• Artificial intelligence
• Problem solving
• General Problem Solver
• Expert systems
• Robotics
• Game AI