InforLorV4, PascalFrancis, Corpus, bibRecord, 000A24

Integer programs and valid inequalities for planning problems

Identifieur interne : 000A24 ( PascalFrancis/Corpus ); précédent : 000A23; suivant : 000A25

Integer programs and valid inequalities for planning problems

Auteurs : A. Bockmayr ; Y. Dimopoulos

Source :

Lecture notes in computer science [ 0302-9743 ] ; 2000.

RBID : Pascal:00-0458296

Descripteurs français

Pascal (Inist)
- Fonction objectif, Fonction complexe, Optimisation sous contrainte, Méthode séparation et évaluation, Logique propositionnelle, Planification, Algorithme recherche, Programmation linéaire, Programmation en nombres entiers, Langage programmation, Problème recherche, Méthode heuristique, Problème combinatoire.

English descriptors

KwdEn :
- Branch and bound method, Combinatorial problem, Complex function, Constrained optimization, Heuristic method, Integer programming, Linear programming, Objective function, Planning, Programming language, Propositional logic, Search algorithm, Search problem.

Abstract

Part of the recent work in AI planning is concerned with the development of algorithms that regard planning as a combinatorial search problem. The underlying representation language is basically propositional logic. While this is adequate for many domains, it is not clear if it remains so for problems that involve numerical constraints, or optimization of complex objective functions. Moreover, the propositional representation imposes restrictions on the domain knowledge that can be utilized by these approaches. In order to address these issues, we propose moving to the more expressive language of Integer Programming (IP). We show how capacity constraints can be easily encoded into linear 0-1 inequalities and how rich forms of domain knowledge can be compactly represented and computationally exploited by IP solvers. Then we introduce a novel heuristic search method based on the linear programming relaxation. Finally, we present the results of our experiments with a classical relaxation-based IP solver and a logic-based 0-1 optimizer.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A01	`01`	`1`		`@0 0302-9743`
A05				`@2 1809`
A08	`01`	`1`	`ENG`	`@1 Integer programs and valid inequalities for planning problems`
A09	`01`	`1`	`ENG`	`@1 Recent advances in AI planning : Durham, 8-10 September 1999`
A11	`01`	`1`		`@1 BOCKMAYR (A.)`
A11	`02`	`1`		`@1 DIMOPOULOS (Y.)`
A12	`01`	`1`		`@1 BIUNDO (Susanne) @9 ed.`
A12	`02`	`1`		`@1 FOX (Maria) @9 ed.`
A14	`01`			`@1 Université Henri Poincaré, LORIA, B.P. 239 @2 54506 Vandœuvre-lès-Nancy @3 FRA @Z 1 aut.`
A14	`02`			`@1 Dep. of Computer Science, University of Cyprus, P.O. Box 20537 @2 1678, Nicosia @3 CYP @Z 2 aut.`
A20				`@1 239-251`
A21				`@1 2000`
A23	`01`			`@0 ENG`
A26	`01`			`@0 3-540-67866-2`
A43	`01`			`@1 INIST @2 16343 @5 354000090077830190`
A44				`@0 0000 @1 © 2000 INIST-CNRS. All rights reserved.`
A45				`@0 21 ref.`
A47	`01`	`1`		`@0 00-0458296`
A60				`@1 P @2 C`
A61				`@0 A`
A64	`01`	`1`		`@0 Lecture notes in computer science`
A66	`01`			`@0 DEU`
A66	`02`			`@0 USA`
C01	`01`		`ENG`	@0 Part of the recent work in AI planning is concerned with the development of algorithms that regard planning as a combinatorial search problem. The underlying representation language is basically propositional logic. While this is adequate for many domains, it is not clear if it remains so for problems that involve numerical constraints, or optimization of complex objective functions. Moreover, the propositional representation imposes restrictions on the domain knowledge that can be utilized by these approaches. In order to address these issues, we propose moving to the more expressive language of Integer Programming (IP). We show how capacity constraints can be easily encoded into linear 0-1 inequalities and how rich forms of domain knowledge can be compactly represented and computationally exploited by IP solvers. Then we introduce a novel heuristic search method based on the linear programming relaxation. Finally, we present the results of our experiments with a classical relaxation-based IP solver and a logic-based 0-1 optimizer.
C02	`01`	`X`		`@0 001D02C06`
C02	`02`	`X`		`@0 001D01A03`
C03	`01`	`X`	`FRE`	`@0 Fonction objectif @5 01`
C03	`01`	`X`	`ENG`	`@0 Objective function @5 01`
C03	`01`	`X`	`SPA`	`@0 Función objetivo @5 01`
C03	`02`	`X`	`FRE`	`@0 Fonction complexe @5 02`
C03	`02`	`X`	`ENG`	`@0 Complex function @5 02`
C03	`02`	`X`	`SPA`	`@0 Función compleja @5 02`
C03	`03`	`X`	`FRE`	`@0 Optimisation sous contrainte @5 03`
C03	`03`	`X`	`ENG`	`@0 Constrained optimization @5 03`
C03	`03`	`X`	`SPA`	`@0 Optimización con restricción @5 03`
C03	`04`	`X`	`FRE`	`@0 Méthode séparation et évaluation @5 04`
C03	`04`	`X`	`ENG`	`@0 Branch and bound method @5 04`
C03	`04`	`X`	`SPA`	`@0 Método branch and bound @5 04`
C03	`05`	`X`	`FRE`	`@0 Logique propositionnelle @5 05`
C03	`05`	`X`	`ENG`	`@0 Propositional logic @5 05`
C03	`05`	`X`	`SPA`	`@0 Lógica proposicional @5 05`
C03	`06`	`X`	`FRE`	`@0 Planification @5 06`
C03	`06`	`X`	`ENG`	`@0 Planning @5 06`
C03	`06`	`X`	`SPA`	`@0 Planificación @5 06`
C03	`07`	`X`	`FRE`	`@0 Algorithme recherche @5 07`
C03	`07`	`X`	`ENG`	`@0 Search algorithm @5 07`
C03	`07`	`X`	`SPA`	`@0 Algoritmo búsqueda @5 07`
C03	`08`	`X`	`FRE`	`@0 Programmation linéaire @5 08`
C03	`08`	`X`	`ENG`	`@0 Linear programming @5 08`
C03	`08`	`X`	`SPA`	`@0 Programación lineal @5 08`
C03	`09`	`X`	`FRE`	`@0 Programmation en nombres entiers @5 09`
C03	`09`	`X`	`ENG`	`@0 Integer programming @5 09`
C03	`09`	`X`	`SPA`	`@0 Programación entera @5 09`
C03	`10`	`X`	`FRE`	`@0 Langage programmation @5 10`
C03	`10`	`X`	`ENG`	`@0 Programming language @5 10`
C03	`10`	`X`	`SPA`	`@0 Lenguaje programación @5 10`
C03	`11`	`X`	`FRE`	`@0 Problème recherche @5 11`
C03	`11`	`X`	`ENG`	`@0 Search problem @5 11`
C03	`11`	`X`	`SPA`	`@0 Problema investigación @5 11`
C03	`12`	`X`	`FRE`	`@0 Méthode heuristique @5 12`
C03	`12`	`X`	`ENG`	`@0 Heuristic method @5 12`
C03	`12`	`X`	`SPA`	`@0 Método heurístico @5 12`
C03	`13`	`X`	`FRE`	`@0 Problème combinatoire @5 13`
C03	`13`	`X`	`ENG`	`@0 Combinatorial problem @5 13`
C03	`13`	`X`	`SPA`	`@0 Problema combinatorio @5 13`
N21				`@1 304`

A30	`01`	`1`	`ENG`	`@1 ECP'99 : European conference on planning @2 5 @3 Durham GBR @4 1999-09-08`

Format Inist (serveur)

NO :	PASCAL 00-0458296 INIST
ET :	Integer programs and valid inequalities for planning problems
AU :	BOCKMAYR (A.); DIMOPOULOS (Y.); BIUNDO (Susanne); FOX (Maria)
AF :	Université Henri Poincaré, LORIA, B.P. 239/54506 Vandœuvre-lès-Nancy/France (1 aut.); Dep. of Computer Science, University of Cyprus, P.O. Box 20537/1678, Nicosia/Chypre (2 aut.)
DT :	Publication en série; Congrès; Niveau analytique
SO :	Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2000; Vol. 1809; Pp. 239-251; Bibl. 21 ref.
LA :	Anglais
EA :	Part of the recent work in AI planning is concerned with the development of algorithms that regard planning as a combinatorial search problem. The underlying representation language is basically propositional logic. While this is adequate for many domains, it is not clear if it remains so for problems that involve numerical constraints, or optimization of complex objective functions. Moreover, the propositional representation imposes restrictions on the domain knowledge that can be utilized by these approaches. In order to address these issues, we propose moving to the more expressive language of Integer Programming (IP). We show how capacity constraints can be easily encoded into linear 0-1 inequalities and how rich forms of domain knowledge can be compactly represented and computationally exploited by IP solvers. Then we introduce a novel heuristic search method based on the linear programming relaxation. Finally, we present the results of our experiments with a classical relaxation-based IP solver and a logic-based 0-1 optimizer.
CC :	001D02C06; 001D01A03
FD :	Fonction objectif; Fonction complexe; Optimisation sous contrainte; Méthode séparation et évaluation; Logique propositionnelle; Planification; Algorithme recherche; Programmation linéaire; Programmation en nombres entiers; Langage programmation; Problème recherche; Méthode heuristique; Problème combinatoire
ED :	Objective function; Complex function; Constrained optimization; Branch and bound method; Propositional logic; Planning; Search algorithm; Linear programming; Integer programming; Programming language; Search problem; Heuristic method; Combinatorial problem
SD :	Función objetivo; Función compleja; Optimización con restricción; Método branch and bound; Lógica proposicional; Planificación; Algoritmo búsqueda; Programación lineal; Programación entera; Lenguaje programación; Problema investigación; Método heurístico; Problema combinatorio
LO :	INIST-16343.354000090077830190
ID :	00-0458296

Links to Exploration step

Pascal:00-0458296

Le document en format XML

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<server><NO>PASCAL 00-0458296 INIST</NO>
<ET>Integer programs and valid inequalities for planning problems</ET>
<AU>BOCKMAYR (A.); DIMOPOULOS (Y.); BIUNDO (Susanne); FOX (Maria)</AU>
<AF>Université Henri Poincaré, LORIA, B.P. 239/54506 Vandœuvre-lès-Nancy/France (1 aut.); Dep. of Computer Science, University of Cyprus, P.O. Box 20537/1678, Nicosia/Chypre (2 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2000; Vol. 1809; Pp. 239-251; Bibl. 21 ref.</SO>
<LA>Anglais</LA>
<EA>Part of the recent work in AI planning is concerned with the development of algorithms that regard planning as a combinatorial search problem. The underlying representation language is basically propositional logic. While this is adequate for many domains, it is not clear if it remains so for problems that involve numerical constraints, or optimization of complex objective functions. Moreover, the propositional representation imposes restrictions on the domain knowledge that can be utilized by these approaches. In order to address these issues, we propose moving to the more expressive language of Integer Programming (IP). We show how capacity constraints can be easily encoded into linear 0-1 inequalities and how rich forms of domain knowledge can be compactly represented and computationally exploited by IP solvers. Then we introduce a novel heuristic search method based on the linear programming relaxation. Finally, we present the results of our experiments with a classical relaxation-based IP solver and a logic-based 0-1 optimizer.</EA>
<CC>001D02C06; 001D01A03</CC>
<FD>Fonction objectif; Fonction complexe; Optimisation sous contrainte; Méthode séparation et évaluation; Logique propositionnelle; Planification; Algorithme recherche; Programmation linéaire; Programmation en nombres entiers; Langage programmation; Problème recherche; Méthode heuristique; Problème combinatoire</FD>
<ED>Objective function; Complex function; Constrained optimization; Branch and bound method; Propositional logic; Planning; Search algorithm; Linear programming; Integer programming; Programming language; Search problem; Heuristic method; Combinatorial problem</ED>
<SD>Función objetivo; Función compleja; Optimización con restricción; Método branch and bound; Lógica proposicional; Planificación; Algoritmo búsqueda; Programación lineal; Programación entera; Lenguaje programación; Problema investigación; Método heurístico; Problema combinatorio</SD>
<LO>INIST-16343.354000090077830190</LO>
<ID>00-0458296</ID>
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Integer programs and valid inequalities for planning problems

Integer programs and valid inequalities for planning problems

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