InforLorV4, PascalFrancis, Corpus, bibRecord, 000064

Une approche inspirée de la recherche de cible mouvante

Identifieur interne : 000064 ( PascalFrancis/Corpus ); précédent : 000063; suivant : 000065

Une approche inspirée de la recherche de cible mouvante

Auteurs : Damien Pellier ; Humbert Fiorino ; Marc Metivier

Source :

Revue d'intelligence artificielle [ 0992-499X ] ; 2013.

RBID : Pascal:13-0216764

Descripteurs français

Pascal (Inist)
- Planification, Système dynamique, Arbre recherche, Cible mobile, Algorithme recherche, Efficacité, Retard, Méthode heuristique, ., Poursuite cible.

English descriptors

KwdEn :
- Delay, Dynamical system, Efficiency, Heuristic method, Moving target, Planning, Search algorithm, Search tree, Target tracking.

Abstract

In this paper, we propose a novel planner, called Moving Goal Planner (MGP) in order to adapt plans when the goal changes over time. This planner draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible starting new searches when the goal changes. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing the actions of the current plan brings MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to adapt incrementally the search tree at each new search that reduces the number of calls to the heuristic function and speeds up the search. Finally, we show evaluation results that demonstrate the effectiveness of our approach.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A09	`01`	`1`	`FRE`	`@1 Apprentissage par renforcement et planification adaptative`
A11	`01`	`1`		`@1 PELLIER (Damien)`
A11	`02`	`1`		`@1 FIORINO (Humbert)`
A11	`03`	`1`		`@1 METIVIER (Marc)`
A12	`01`	`1`		`@1 ZANUTTINI (Bruno) @9 ed.`
A12	`02`	`1`		`@1 LAURENT (Guillaume) @9 ed.`
A12	`03`	`1`		`@1 BUFFET (Olivier) @9 ed.`
A14	`01`			`@1 Laboratoire d'Informatique de Paris Descartes Universite Paris Descartes 45, rue des Saints Pères @2 75006 Paris @3 FRA @Z 1 aut. @Z 3 aut.`
A14	`02`			`@1 Laboratoire d'Informatique de Grenoble Université Joseph Fourier 110, avenue de la Chimie @2 38400 Saint-Martin-d'Hères @3 FRA @Z 2 aut.`
A15	`01`			`@1 Greyc/UCBN @2 Caen @3 FRA @Z 1 aut.`
A15	`02`			`@1 Institut FEMTO-ST/ENSMM @2 Besançon @3 FRA @Z 2 aut.`
A15	`03`			`@1 LORIA/INRIA @2 Nancy @3 FRA @Z 3 aut.`
A20				`@2 151, 217-242 [27 p.]`
A21				`@1 2013`
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A24	`01`			`@0 eng`
A43	`01`			`@1 INIST @2 21320 @5 354000173351010040`
A44				`@0 0000 @1 © 2013 INIST-CNRS. All rights reserved.`
A45				`@0 1 p.3/4`
A47	`01`	`1`		`@0 13-0216764`
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C01	`01`		`ENG`	@0 In this paper, we propose a novel planner, called Moving Goal Planner (MGP) in order to adapt plans when the goal changes over time. This planner draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible starting new searches when the goal changes. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing the actions of the current plan brings MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to adapt incrementally the search tree at each new search that reduces the number of calls to the heuristic function and speeds up the search. Finally, we show evaluation results that demonstrate the effectiveness of our approach.
C02	`01`	`X`		`@0 001D02C02`
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C03	`01`	`X`	`ENG`	`@0 Planning @5 06`
C03	`01`	`X`	`SPA`	`@0 Planificación @5 06`
C03	`02`	`X`	`FRE`	`@0 Système dynamique @5 07`
C03	`02`	`X`	`ENG`	`@0 Dynamical system @5 07`
C03	`02`	`X`	`SPA`	`@0 Sistema dinámico @5 07`
C03	`03`	`X`	`FRE`	`@0 Arbre recherche @5 08`
C03	`03`	`X`	`ENG`	`@0 Search tree @5 08`
C03	`03`	`X`	`SPA`	`@0 Arbol investigación @5 08`
C03	`04`	`X`	`FRE`	`@0 Cible mobile @5 18`
C03	`04`	`X`	`ENG`	`@0 Moving target @5 18`
C03	`04`	`X`	`SPA`	`@0 Blanco móvil @5 18`
C03	`05`	`X`	`FRE`	`@0 Algorithme recherche @5 23`
C03	`05`	`X`	`ENG`	`@0 Search algorithm @5 23`
C03	`05`	`X`	`SPA`	`@0 Algoritmo búsqueda @5 23`
C03	`06`	`X`	`FRE`	`@0 Efficacité @5 24`
C03	`06`	`X`	`ENG`	`@0 Efficiency @5 24`
C03	`06`	`X`	`SPA`	`@0 Eficacia @5 24`
C03	`07`	`X`	`FRE`	`@0 Retard @5 25`
C03	`07`	`X`	`ENG`	`@0 Delay @5 25`
C03	`07`	`X`	`SPA`	`@0 Retraso @5 25`
C03	`08`	`X`	`FRE`	`@0 Méthode heuristique @5 26`
C03	`08`	`X`	`ENG`	`@0 Heuristic method @5 26`
C03	`08`	`X`	`SPA`	`@0 Método heurístico @5 26`
C03	`09`	`X`	`FRE`	`@0 . @4 INC @5 82`
C03	`10`	`X`	`FRE`	`@0 Poursuite cible @4 CD @5 96`
C03	`10`	`X`	`ENG`	`@0 Target tracking @4 CD @5 96`
C03	`10`	`X`	`SPA`	`@0 Persecución de blanco @4 CD @5 96`
N21				`@1 203`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

Format Inist (serveur)

NO :	PASCAL 13-0216764 INIST
FT :	Une approche inspirée de la recherche de cible mouvante
ET :	(Planning when the goal changes: a moving target search approach)
AU :	PELLIER (Damien); FIORINO (Humbert); METIVIER (Marc); ZANUTTINI (Bruno); LAURENT (Guillaume); BUFFET (Olivier)
AF :	Laboratoire d'Informatique de Paris Descartes Universite Paris Descartes 45, rue des Saints Pères/75006 Paris/France (1 aut., 3 aut.); Laboratoire d'Informatique de Grenoble Université Joseph Fourier 110, avenue de la Chimie/38400 Saint-Martin-d'Hères/France (2 aut.); Greyc/UCBN/Caen/France (1 aut.); Institut FEMTO-ST/ENSMM/Besançon/France (2 aut.); LORIA/INRIA/Nancy/France (3 aut.)
DT :	Publication en série; Niveau analytique
SO :	Revue d'intelligence artificielle; ISSN 0992-499X; France; Da. 2013; Vol. 27; No. 2; 151, 217-242 [27 p.]; Abs. anglais; Bibl. 1 p.3/4
LA :	Français
EA :	In this paper, we propose a novel planner, called Moving Goal Planner (MGP) in order to adapt plans when the goal changes over time. This planner draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible starting new searches when the goal changes. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing the actions of the current plan brings MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to adapt incrementally the search tree at each new search that reduces the number of calls to the heuristic function and speeds up the search. Finally, we show evaluation results that demonstrate the effectiveness of our approach.
CC :	001D02C02
FD :	Planification; Système dynamique; Arbre recherche; Cible mobile; Algorithme recherche; Efficacité; Retard; Méthode heuristique; .; Poursuite cible
ED :	Planning; Dynamical system; Search tree; Moving target; Search algorithm; Efficiency; Delay; Heuristic method; Target tracking
SD :	Planificación; Sistema dinámico; Arbol investigación; Blanco móvil; Algoritmo búsqueda; Eficacia; Retraso; Método heurístico; Persecución de blanco
LO :	INIST-21320.354000173351010040
ID :	13-0216764

Links to Exploration step

Pascal:13-0216764

Le document en format XML

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<front><div type="abstract" xml:lang="en">In this paper, we propose a novel planner, called Moving Goal Planner (MGP) in order to adapt plans when the goal changes over time. This planner draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible starting new searches when the goal changes. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing the actions of the current plan brings MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to adapt incrementally the search tree at each new search that reduces the number of calls to the heuristic function and speeds up the search. Finally, we show evaluation results that demonstrate the effectiveness of our approach.</div>
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<FT>Une approche inspirée de la recherche de cible mouvante</FT>
<ET>(Planning when the goal changes: a moving target search approach)</ET>
<AU>PELLIER (Damien); FIORINO (Humbert); METIVIER (Marc); ZANUTTINI (Bruno); LAURENT (Guillaume); BUFFET (Olivier)</AU>
<AF>Laboratoire d'Informatique de Paris Descartes Universite Paris Descartes 45, rue des Saints Pères/75006 Paris/France (1 aut., 3 aut.); Laboratoire d'Informatique de Grenoble Université Joseph Fourier 110, avenue de la Chimie/38400 Saint-Martin-d'Hères/France (2 aut.); Greyc/UCBN/Caen/France (1 aut.); Institut FEMTO-ST/ENSMM/Besançon/France (2 aut.); LORIA/INRIA/Nancy/France (3 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Revue d'intelligence artificielle; ISSN 0992-499X; France; Da. 2013; Vol. 27; No. 2; 151, 217-242 [27 p.]; Abs. anglais; Bibl. 1 p.3/4</SO>
<LA>Français</LA>
<EA>In this paper, we propose a novel planner, called Moving Goal Planner (MGP) in order to adapt plans when the goal changes over time. This planner draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible starting new searches when the goal changes. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing the actions of the current plan brings MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to adapt incrementally the search tree at each new search that reduces the number of calls to the heuristic function and speeds up the search. Finally, we show evaluation results that demonstrate the effectiveness of our approach.</EA>
<CC>001D02C02</CC>
<FD>Planification; Système dynamique; Arbre recherche; Cible mobile; Algorithme recherche; Efficacité; Retard; Méthode heuristique; .; Poursuite cible</FD>
<ED>Planning; Dynamical system; Search tree; Moving target; Search algorithm; Efficiency; Delay; Heuristic method; Target tracking</ED>
<SD>Planificación; Sistema dinámico; Arbol investigación; Blanco móvil; Algoritmo búsqueda; Eficacia; Retraso; Método heurístico; Persecución de blanco</SD>
<LO>INIST-21320.354000173351010040</LO>
<ID>13-0216764</ID>
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Une approche inspirée de la recherche de cible mouvante

Une approche inspirée de la recherche de cible mouvante

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