Best-first frontier search with delayed duplicate detection

  • Authors:

  • Richard E. Korf

  • Affiliations:

  • Computer Science Department, University of California, Los Angeles, Los Angeles, CA

  • Venue:
  • AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
  • Year:
  • 2004

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Abstract

Best-first search is limited by the memory needed to store the Open and Closed lists, primarily to detect duplicate nodes. Magnetic disks provide vastly more storage, but random access of a disk is extremely slow. Instead of checking generated nodes immediately against existing nodes in a hash table, delayed duplicate detection (DDD) appends them to a file, then periodically removes the duplicate nodes using only sequential disk accesses. Frontier search saves storage in a best-first search by storing only the Open list and not the Closed list. The main contributions of this paper are to provide a scalable implementation of DDD, to combine it with frontier search, and to extend it to more general best-first searches such as A*. We illustrate these ideas by performing complete breadth-first searches of sliding-tile puzzles up to the 3×5 Fourteen Puzzle. For the 4-peg Towers of Hanoi problem, we perform complete searches with up to 20 disks, searching a space of over a trillion nodes, and discover a surprising anomaly concerning the problem-space diameter of the 15 and 20-disk problems. We also verify the presumed optimal solution lengths for up to 24 disks. In addition, we implement A* with DDD on the Fifteen Puzzle. Finally, we present a scalable implementation of DDD based on hashing rather than sorting.