Constraints and AI planning

Alexander Nareyek*, Eugene C. Freuder, Robert Fourer, Enrico Giunchiglia, Robert P. Goldman, Henry Kautz, Jussi Rintanen, Austin Tate

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

46 Scopus citations


The interplay of constraint and planning, and the differences between propositional satisfiability (SAT), integer programming (IP) and constraint programming (CP) are discussed. Constraint optimization requires an additional function that assigns a quality value to a solution and tries to find a solution that maximizes this value. The hierarchical task network planning (HTN) exhibits the capability to stipulate global constraints on plans, meshing well with the needs of systems that combine planning and constraint satisfaction. The expressive powers of HTN planning makes it easy to specify global constraints and make them available to constraint solvers.

Original languageEnglish (US)
Pages (from-to)62-72
Number of pages11
JournalIEEE Intelligent Systems
Issue number2
StatePublished - 2005

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence


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