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Means-ends analysis
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Means-ends analysis : ウィキペディア英語版
Means-ends analysis
Means-Ends Analysis〔Simon, H. A. (1981). ''The sciences of the artificial.'' Cambridge, Mass: MIT Press.〕 (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.〔Eugene, K and Carman, W.C. (2006). ''Analysis of means-end chain data in marketing research.'' Cambridge, Mass: MIT Press.〕 It is also a way to clarify one's thoughts when embarking on a mathematical proof.
== Problem-solving as search ==
An important aspect of intelligent behavior as studied in AI is ''goal-based'' problem solving, a framework in which the solution of 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).

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