Means-ends analysis

Means-ends analysis (MEA) is a problem solving technique used commonly in artificial intelligence (AI) for limiting search in AI programs. Means-ends analysis (MEA) is a problem solving technique used commonly in artificial intelligence (AI) for limiting search in AI programs. It is also a technique used at least since the 1950s as a creativity tool, most frequently mentioned in engineering books on design methods. MEA is also related to means-ends chain approach used commonly in consumer behavior analysis. It is also a way to clarify one's thoughts when embarking on a mathematical proof. An important aspect of intelligent behavior as studied in AI is goal-based problem solving, a framework in which the solution to a problem can be described by finding a sequence of actions that lead to a desirable goal. A goal-seeking system is supposed to be connected to its outside environment by sensory channels through which it receives information about the environment and motor channels through which it acts on the environment. (The term 'afferent' is used to describe 'inward' sensory flows, and 'efferent' is used to describe 'outward' motor commands.) In addition, the system has some means of storing in a memory information about the state of the environment (afferent information) and information about actions (efferent information). Ability to attain goals depends on building up associations, simple or complex, between particular changes in states and particular actions that will bring these changes about. Search is the process of discovery and assembly of sequences of actions that will lead from a given state to a desired state. While this strategy may be appropriate for machine learning and problem solving, it is not always suggested for humans (e.g. cognitive load theory and its implications). The MEA technique is a strategy to control search in problem-solving. Given a current state and a goal state, an action is chosen which will reduce the difference between the two. The action is performed on the current state to produce a new state, and the process is recursively applied to this new state and the goal state.