means-ends analysis , MEA (abbreviation)

In contrast, older children appear to employ a rudimentary form of Means-Ends Analysis.

Some examples are means-ends analysis (which may calculate the difference between each available option and the goal state) and a type of hill-climbing.

Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.

He suggests problem solving by means-ends analysis requires a relatively large amount of cognitive processing capacity, which may not be devoted to schema construction.

Studying learners as they solved problems, he and his associates found that learners often use a problem solving strategy called means-ends analysis.

On the basis of a series of computational models, it appears that the older children deploy a rudimentary form of Means-Ends Analysis.

The user defined objects and operations that could be done on the objects, and GPS generated heuristics by Means-ends analysis in order to solve problems.

Drew McDermott (1996) A Heuristic Estimator for Means-Ends Analysis in Planning.

Klahr argued that although there are large adult-child differences in overall problem-solving performance, even preschoolers have rudimentary forms of strategies such as means-ends analysis that rely on the use of goals.

Means-Ends Analysis (MEA) is a problem solving technique used commonly in Artificial Intelligence (AI) for limiting search in AI programs.

His inventions included: list processing, the most important programming paradigm used by AI ever since; the application of means-ends analysis to general reasoning (or "reasoning as search"); and the use of heuristics to limit the search space.

This work included the General Problem Solver, a highly influential implementation of means-ends analysis, and the physical symbol systems hypothesis, the controversial philosophical assertion that all intelligent behavior could be reduced to the kind of symbol manipulation that Newell's programs demonstrated.