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

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

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
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
Publication status	Published - 2005

Access to Document

10.1109/MIS.2005.25

Cite this

@article{8aa2f1b0b94e433b9f1cb2c55986992e,

title = "Constraints and AI planning",

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.",

author = "Alexander Nareyek and Freuder, \{Eugene C.\} and Robert Fourer and Enrico Giunchiglia and Goldman, \{Robert P.\} and Henry Kautz and Jussi Rintanen and Austin Tate",

year = "2005",

doi = "10.1109/MIS.2005.25",

language = "English",

volume = "20",

pages = "62--72",

journal = "IEEE Intelligent Systems",

issn = "1541-1672",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "2",

}

TY - JOUR

T1 - Constraints and AI planning

AU - Nareyek, Alexander

AU - Freuder, Eugene C.

AU - Fourer, Robert

AU - Giunchiglia, Enrico

AU - Goldman, Robert P.

AU - Kautz, Henry

AU - Rintanen, Jussi

AU - Tate, Austin

PY - 2005

Y1 - 2005

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.

UR - https://www.scopus.com/pages/publications/17444413791

U2 - 10.1109/MIS.2005.25

DO - 10.1109/MIS.2005.25

M3 - Review article

AN - SCOPUS:17444413791

SN - 1541-1672

VL - 20

SP - 62

EP - 72

JO - IEEE Intelligent Systems

JF - IEEE Intelligent Systems

IS - 2

ER -

Constraints and AI planning

Abstract

Access to Document

Other files and links

Fingerprint

Cite this