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- W1505609243 abstract "Abstract This paper analyzes the average number of nodes expanded by A∗ as a function of the accuracy of its heuristic estimates, by treating the errors h∗ - h as random variables whose distribution may vary over the nodes in the graph. The search model consists of an m-ary tree with unit branch costs and a unique goal state situated at a distance N from the root. The main result states that if the typical error grows like φ(h∗) then the mean complexity of A∗ grows approximately like G ( N )exp[ cφ ( N )], where c is a positive constant and G(N) is O(N 2 ). Thus, a necessary and sufficient condition for maintaining polynomial search complexity is that A∗ be guided by heuristics with logarithmic precision, e.g. φ ( N ) = (log N ) k . A∗ is shown to make much greater use of its heuristic knowledge than a backtracking procedure would under similar conditions." @default.
- W1505609243 created "2016-06-24" @default.
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- W1505609243 date "1983-01-01" @default.
- W1505609243 modified "2023-10-17" @default.
- W1505609243 title "Knowledge versus search" @default.
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- W1505609243 doi "https://doi.org/10.1016/0004-3702(83)90013-9" @default.
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