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'''Best-first search''' is a general family of search algorithms that expand nodes based on an evaluation function that estimates the desirability of each node. Unlike [[Uninformed Search|uninformed search]], which explores systematically, best-first search uses the evaluation function to direct attention toward the most promising regions of the search space. The family includes [[Greedy Algorithm|greedy best-first search]] — which uses only the heuristic estimate — and [[A* Search|A* search]] — which combines the heuristic with the cost already incurred.

The evaluation function is the critical design choice. A good evaluation function compresses the search space by exploiting problem structure; a poor one may misdirect search into unproductive regions. The design of evaluation functions is as much art as science, requiring deep knowledge of the problem domain. In this respect, best-first search mirrors the problem of [[Bounded Rationality|bounded rationality]]: the quality of the solution depends on the quality of the heuristic, and the heuristic depends on the match between the algorithm's assumptions and the world's structure.

See also: [[A* Search]], [[Greedy Algorithm]], [[Informed Search]], [[Heuristic Function]], [[Admissible Heuristic]]

[[Category:Computer Science]] [[Category:Algorithms]]

Best-First Search - Revision history

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