Constraints and AI planning

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

doi:10.1109/MIS.2005.25

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

Industrial Engineering and Management Sciences

Research output: Contribution to journal › Review article › peer-review

48 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	62-72
Number of pages	11
Journal	IEEE Intelligent Systems
Volume	20
Issue number	2
DOIs	https://doi.org/10.1109/MIS.2005.25
State	Published - 2005

ASJC Scopus subject areas

Computer Networks and Communications
Artificial Intelligence

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10.1109/MIS.2005.25

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AU - Freuder, Eugene C.

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AU - Giunchiglia, Enrico

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AU - Kautz, Henry

AU - Rintanen, Jussi

AU - Tate, Austin

PY - 2005

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N2 - 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.

AB - 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.

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Constraints and AI planning

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ASJC Scopus subject areas

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