AbstractD. Furcy and S. Koenig. Limited Discrepancy Beam Search. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 125-131, 2005.
Abstract: Beam search reduces the memory consumption of best-first search at the cost of finding longer paths but its memory consumption can still exceed the given memory capacity quickly. We therefore develop BULB (Beam search Using Limited discrepancy Backtracking), a complete memory-bounded search method that is able to solve more problem instances of large search problems than beam search and does so with a reasonable runtime. At the same time, BULB tends to find shorter paths than beam search because it is able to use larger beam widths without running out of memory. We demonstrate these properties of BULB experimentally for three standard benchmark domains.
Download the paper in pdf.
Download the paper in gzipped postscript (large download time).
Many publishers do not want authors to make their papers available electronically after the papers have been published. Please use the electronic versions provided here only if hardcopies are not yet available. If you have comments on any of these papers, please send me an email! Also, please send me your papers if we have common interests.
This page was automatically created by a bibliography maintenance system that was developed as part of an undergraduate research project, advised by Sven Koenig.