A Search Algorithm
Updated 2026-08-09
INTRODUCTION
English translation pending.
CORE DEFINITION
Introduced by Peter Hart, Nils Nilsson and Bertram Raphael, the algorithm finds a least-cost path by ranking frontier nodes with f(n) = g(n) + h(n), where g is the exact cost from the start and h is an admissible estimate of the cost to the goal. If h never overestimates the true remaining cost, the search is guaranteed to return an optimal path; with a consistent heuristic it never needs to reopen closed nodes. Setting h to zero reduces it to Dijkstra's algorithm, while weighting h more heavily trades optimality for speed.
SCAFFOLDING EFFECT
Reduce cognitive load
- Weight past and future: combine sunk cost with an estimate of remaining distance before choosing a move. - Guard the estimate: check that your forecast never overstates the work still ahead. - Tune the balance: shift weight between progress made and distance left as uncertainty changes.
Anchor fast decisions
The frontier is ordered by total estimated cost, so the algorithm always explores the most promising partial path first. Because the heuristic never overestimates, any path that has already exceeded the estimate to the goal can be safely abandoned, and the search avoids expanding the broad uniform regions that make blind search expensive. Optimality and speed both follow from that admissibility condition.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/A*_search_algorithmverified
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