Landmark — where it appears
Named by 3 essays across one field — each of them below, with the objects they name alongside it.
An estimate borrowed from an easier problem
On a grid where every step costs one, the straight-line distance to the goal cuts a search from 543 cells to 325. On terrain where steps cost between one and nine it cuts 1,572 to 1,550, because it still believes every step costs one. Four exact distance tables, computed once, cut the same search to 252 — and cost 6,328 reads to build, so they pay for themselves on the fifth query.
Where the landmarks stand
Four tables of exact distances, each from a chosen cell, turn a straight-line estimate that barely helps on rough terrain into one that cuts a search by a factor of six. Averaged over 1,200 queries on eight maps, the same four tables expand 126 cells a query when their cells are the map's corners and 423 when they are near its centre. The standard choice, each landmark as far as possible from the ones before, expands 141 and loses to the corners on all eight maps. Moving four landmarks to the right places buys more than doubling their number.
What the queries know that the map does not
A greedy rule that chooses landmark cells by rerunning a sample of past queries needs two hundred of them to draw level with a rule that reads only the map — and what it finally chooses, on map after map, is the four corners. Give the queries a destination instead of scattering them, and twenty are enough to beat the corners by 29% on eight maps out of eight. A query log is worth reading exactly to the extent that it is not uniform.
Named alongside it
The objects these essays reach for when they reach for this one.
Break-evenHeuristic searchPreprocessingShortest pathTriangle inequalityAdmissibilityParameter choicePotential functionSearch frontierDijkstra's algorithmGreedy algorithmLower bound