PLANMINE: Predicting plan failures using sequence mining

Mohammed J. Zaki, Neal Lesh, Mitsunori Ogihara

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


This paper presents the PLANMINE sequence mining algorithm to extract patterns of events that predict failures in databases of plan executions. New techniques were needed because previous data mining algorithms were overwhelmed by the staggering number of very frequent, but entirely unpredictive patterns that exist in the plan database. This paper combines several techniques for pruning out unpredictive and redundant patterns which reduce the size of the returned rule set by more than three orders of magnitude. PLANMINE has also been fully integrated into two real-world planning systems. We experimentally evaluate the rules discovered by PLANMINE, and show that they are extremely useful for understanding and improving plans, as well as for building monitors that raise alarms before failures happen.

Original languageEnglish (US)
Pages (from-to)421-446
Number of pages26
JournalArtificial Intelligence Review
Issue number6
StatePublished - Dec 2000
Externally publishedYes


  • Plan monitoring
  • Predicting failures
  • Sequence mining

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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