@inproceedings{ea3e5ba309c247fc84d763be34b9ea41,
title = "Estimating search tree size with duplicate detection",
abstract = "In this paper we introduce Stratified Sampling with Duplicate Detection (SSDD), an algorithm for estimating the number of state expansions performed by heuristic search algorithms seeking solutions in state spaces represented by undirected graphs. SSDD is general and can be applied to estimate other state-space properties. We test SSDD on two tasks: (i) prediction of the number of A* expansions in a given f-layer when using a consistent heuristic function, and (ii) prediction of the state-space radius. SSDD has the asymptotic guarantee of producing perfect estimates in both tasks. Our empirical results show that in task (i) SSDD produces good estimates in all four domains tested, being in most cases orders of magnitude more accurate than a competing scheme, and in task (ii) SSDD quickly produces accurate estimates of the radii of the 4×4 Sliding-Tile Puzzle and the 3×3×3 Rubik{\textquoteright}s Cube.",
author = "Lelis, {Levi H.S.} and Roni Stern and Sturtevant, {Nathan R.}",
note = "Publisher Copyright: Copyright {\textcopyright} 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 7th Annual Symposium on Combinatorial Search, SoCS 2014 ; Conference date: 15-08-2014 Through 17-08-2014",
year = "2014",
month = jan,
day = "1",
language = "English",
series = "Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014",
publisher = "AAAI press",
pages = "114--122",
editor = "Stefan Edelkamp and Roman Bartak",
booktitle = "Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014",
}