Limited discrepancy beam search

  • Authors:

  • David Furcy;Sven Koenig

  • Affiliations:

  • University of Wisconsin Oshkosh, Computer Science Department, Oshkosh, WI;University of Southern California, Computer Science Department, Los Angeles, CA

  • Venue:
  • IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
  • Year:
  • 2005

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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.