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Means–ends analysis

Means–ends analysis (MEA) is a problem solving technique used commonly in artificial intelligence (AI) for limiting search in AI programs. MEA was designed by two scientist Allen Newel and Herbert A. Simon in 1957, lateron, the idea of MEA led to the General Problem Solver, with J.C Shaw.

Works, Applications & Art

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Explore the main themes, entities and connections around Means–ends analysis. Start with the topic map, then use the sections below for research and deeper semantic analysis.

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Topics to explore

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Overview

Problem-solving as search

How it works

AI use

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Means–ends analysis

Nodes21
Edges20
Triples0
Avg. degree1.9
Density0.095238
Components1

How this topic connects Entity context

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Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

mea problem also search means used actions state technique system differences solving solver ends analysis problem-solving ai goal knowledge commonly

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
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