Disjoint pattern database heuristics

Richard E. Korf, Ariel Felner

Research output: Contribution to journalArticlepeer-review

168 Scopus citations


We describe a new technique for designing more accurate admissible heuristic evaluation functions, based on pattern databases [J. Culberson, J. Schaeffer, Comput. Intelligence 14 (3) (1998) 318-334]. While many heuristics, such as Manhattan distance, compute the cost of solving individual subgoals independently, pattern databases consider the cost of solving multiple subgoals simultaneously. Existing work on pattern databases allows combining values from different pattern databases by taking their maximum. If the subgoals can be divided into disjoint subsets so that each operator only affects subgoals in one subset, then we can add the pattern-database values for each subset, resulting in a more accurate admissible heuristic function. We used this technique to improve performance on the Fifteen Puzzle by a factor of over 2000, and to find optimal solutions to 50 random instances of the Twenty-Four Puzzle.

Original languageEnglish
Pages (from-to)9-22
Number of pages14
JournalArtificial Intelligence
Issue number1-2
StatePublished - 1 Jan 2002
Externally publishedYes


  • Fifteen Puzzle
  • Heuristic evaluation functions
  • Heuristic search
  • Pattern databases
  • Problem solving
  • Rubik's Cube
  • Single-agent search
  • Sliding-tile puzzles
  • Twenty-Four Puzzle

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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