KBFS: K-Best-First Search

Ariel Felner, Sarit Kraus, Richard E. Korf

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

We introduce a new algorithm, K-best-first search (KBFS), which is a generalization of the well known best-first search (BFS). In KBFS, each iteration simultaneously expands the K best nodes from the open-list (rather than just the best as in BFS). We claim that KBFS outperforms BFS in domains where the heuristic function has large errors in estimation of the real distance to the goal state or does not predict dead-ends in the search tree. We present empirical results that confirm this claim and show that KBFS outperforms BFS by a factor of 15 on random trees with dead-ends, and by a factor of 2 and 7 on the Fifteen and Twenty-Four tile puzzles, respectively. KBFS also finds better solutions than BFS and hill-climbing for the number partitioning problem. KBFS is only appropriate for finding approximate solutions with inadmissible heuristic functions.

Original languageEnglish
Pages (from-to)19-39
Number of pages21
JournalAnnals of Mathematics and Artificial Intelligence
Volume39
Issue number1-2
DOIs
StatePublished - 1 Sep 2003
Externally publishedYes

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

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