Une approche inspirée de la recherche de cible mouvante
Identifieur interne : 001685 ( Main/Exploration ); précédent : 001684; suivant : 001686Une approche inspirée de la recherche de cible mouvante
Auteurs : Damien Pellier [France] ; Humbert Fiorino [France] ; Marc Metivier [France]Source :
- Revue d'intelligence artificielle [ 0992-499X ] ; 2013.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
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.
Affiliations:
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Pères</s1><s2>75006 Paris</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Île-de-France</region><settlement type="city">Paris</settlement></placeName></affiliation></author><author><name sortKey="Fiorino, Humbert" sort="Fiorino, Humbert" uniqKey="Fiorino H" first="Humbert" last="Fiorino">Humbert Fiorino</name><affiliation wicri:level="3"><inist:fA14 i1="02"><s1>Laboratoire d'Informatique de Grenoble Université Joseph Fourier 110, avenue de la Chimie</s1><s2>38400 Saint-Martin-d'Hères</s2><s3>FRA</s3><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Auvergne-Rhône-Alpes</region><region type="old region" nuts="2">Rhône-Alpes</region><settlement type="city">Saint-Martin-d'Hères</settlement></placeName></affiliation></author><author><name sortKey="Metivier, Marc" sort="Metivier, Marc" uniqKey="Metivier M" first="Marc" last="Metivier">Marc Metivier</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>Laboratoire d'Informatique de Paris Descartes Universite Paris Descartes 45, rue des Saints Pères</s1><s2>75006 Paris</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Île-de-France</region><settlement type="city">Paris</settlement></placeName></affiliation></author></analytic><series><title level="j" type="main">Revue d'intelligence artificielle</title><title level="j" type="abbreviated">Rev. intell. artif.</title><idno type="ISSN">0992-499X</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Revue d'intelligence artificielle</title><title level="j" type="abbreviated">Rev. intell. artif.</title><idno type="ISSN">0992-499X</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Delay</term><term>Dynamical system</term><term>Efficiency</term><term>Heuristic method</term><term>Moving target</term><term>Planning</term><term>Search algorithm</term><term>Search tree</term><term>Target tracking</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Planification</term><term>Système dynamique</term><term>Arbre recherche</term><term>Cible mobile</term><term>Algorithme recherche</term><term>Efficacité</term><term>Retard</term><term>Méthode heuristique</term><term>.</term><term>Poursuite cible</term></keywords></textClass></profileDesc></teiHeader><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></front></TEI><affiliations><list><country><li>France</li></country><region><li>Auvergne-Rhône-Alpes</li><li>Rhône-Alpes</li><li>Île-de-France</li></region><settlement><li>Paris</li><li>Saint-Martin-d'Hères</li></settlement></list><tree><country name="France"><region name="Île-de-France"><name sortKey="Pellier, Damien" sort="Pellier, Damien" uniqKey="Pellier D" first="Damien" last="Pellier">Damien Pellier</name></region><name sortKey="Fiorino, Humbert" sort="Fiorino, Humbert" uniqKey="Fiorino H" first="Humbert" last="Fiorino">Humbert Fiorino</name><name sortKey="Metivier, Marc" sort="Metivier, Marc" uniqKey="Metivier M" first="Marc" last="Metivier">Marc Metivier</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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