Constraints and AI planning

被引：28

作者：

Nareyek, A

Freuder, EC

Fourer, R

Giunchiglia, E

Goldman, RP

Kautz, H

Rintanen, J

Tate, A

机构：

[1] Natl Univ Ireland Univ Coll Cork, Cork Constraint Computat Ctr, Cork, Ireland

[2] Northwestern Univ, Dept Ind Engn & Management Sci, Evanston, IL 60208 USA

[3] NW Argonne Optimization Technol Ctr, Argonne, IL USA

[4] Univ Genoa, DIST, Dept Commun Comp & Syst Sci, I-16145 Genoa, Italy

[5] Smart Informat Flow Technol LLC, Minneapolis, MN 55401 USA

[6] Univ Washington, Dept Comp Sci & Engn, Seattle, WA 98195 USA

[7] Univ Freiburg, Inst Informat, AG KI, D-79110 Freiburg, Germany

[8] Univ Edinburgh, AIAI, Edinburgh EH8 9LE, Midlothian, Scotland

来源：

IEEE INTELLIGENT SYSTEMS | 2005年 / 20卷 / 02期

关键词：

D O I：

10.1109/MIS.2005.25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：62 / 72

页数：11

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