Etude de différentes combinaisons de comportements adaptatives
Identifieur interne :
000439 ( PascalFrancis/Corpus );
précédent :
000438;
suivant :
000440
Etude de différentes combinaisons de comportements adaptatives
Auteurs : Olivier Buffet ;
Alain Dutech ;
Francois CharpilletSource :
-
Revue d'intelligence artificielle [ 0992-499X ] ; 2006.
RBID : Pascal:06-0329056
Descripteurs français
English descriptors
Abstract
This article focusses on the automated synthesis of agents in an uncertain environment, working in the setting of Reinforcement Learning, and more precisely of Partially Observable Markov Decision Processes. The agents (with no model of their environment and no short-term memory) are facing multiple motivations/goals simultaneously, a problem related to the field of Action Selection. We propose and evaluate various Action Selection architectures. They all combine already known basic behaviors in an adaptive manner, by learning the tuning of the combination, so as to maximize the agent's payoff. The logical continuation of this work is to automate the selection and design of the basic behaviors themselves.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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| A08 | 01 | 1 | FRE | @1 Etude de différentes combinaisons de comportements adaptatives |
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| A09 | 01 | 1 | FRE | @1 Décision et planification dans l'incertain |
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| A11 | 01 | 1 | | @1 BUFFET (Olivier) |
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| A11 | 02 | 1 | | @1 DUTECH (Alain) |
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| A11 | 03 | 1 | | @1 CHARPILLET (Francois) |
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| A12 | 01 | 1 | | @1 CHARPILLET (F.) @9 ed. |
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| A12 | 02 | 1 | | @1 GARCIA (F.) @9 ed. |
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| A12 | 03 | 1 | | @1 PERNY (Patrice) @9 ed. |
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| A12 | 04 | 1 | | @1 SIGAUD (Olivier) @9 ed. |
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| A14 | 01 | | | @1 LORIA - INRIA-Lorraine / Campus Scientifique - B.P. 239 @2 54506 Vandoeuvre-lès-Nancy @3 FRA @Z 1 aut. @Z 2 aut. @Z 3 aut. |
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| A14 | 02 | | | @1 National ICT Australia & The Autralian National Vniversity RSISE Building 115 - ANU/ @2 Canberra ACT 0200 @3 AUS @Z 1 aut. |
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| A15 | 01 | | | @1 LORIA-INRIA @2 Nancy @3 FRA @Z 1 aut. |
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| A15 | 02 | | | @1 INRA-MIA @2 Toulouse @3 FRA @Z 2 aut. |
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| A15 | 03 | | | @1 LIP6 @2 Paris @3 FRA @Z 3 aut. @Z 4 aut. |
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| A43 | 01 | | | @1 INIST @2 21320 @5 354000142556580060 |
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| A44 | | | | @0 0000 @1 © 2006 INIST-CNRS. All rights reserved. |
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| A45 | | | | @0 1 p.3/4 |
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| A47 | 01 | 1 | | @0 06-0329056 |
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| A64 | 01 | 1 | | @0 Revue d'intelligence artificielle |
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| C01 | 01 | | ENG | @0 This article focusses on the automated synthesis of agents in an uncertain environment, working in the setting of Reinforcement Learning, and more precisely of Partially Observable Markov Decision Processes. The agents (with no model of their environment and no short-term memory) are facing multiple motivations/goals simultaneously, a problem related to the field of Action Selection. We propose and evaluate various Action Selection architectures. They all combine already known basic behaviors in an adaptive manner, by learning the tuning of the combination, so as to maximize the agent's payoff. The logical continuation of this work is to automate the selection and design of the basic behaviors themselves. |
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| C03 | 01 | X | ENG | @0 Uncertain system @5 06 |
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| C03 | 01 | X | SPA | @0 Sistema incierto @5 06 |
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| C03 | 02 | X | FRE | @0 Apprentissage renforcé @5 07 |
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| C03 | 02 | X | ENG | @0 Reinforcement learning @5 07 |
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| C03 | 02 | X | SPA | @0 Aprendizaje reforzado @5 07 |
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| C03 | 03 | X | FRE | @0 Long terme @5 08 |
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| C03 | 03 | X | ENG | @0 Long term @5 08 |
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| C03 | 03 | X | SPA | @0 Largo plazo @5 08 |
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| C03 | 04 | X | FRE | @0 Motivation @5 09 |
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| C03 | 04 | X | ENG | @0 Motivation @5 09 |
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| C03 | 04 | X | SPA | @0 Motivación @5 09 |
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| C03 | 06 | X | ENG | @0 Observable @5 18 |
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| C03 | 06 | X | SPA | @0 Observable @5 18 |
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| C03 | 07 | X | FRE | @0 Décision Markov @5 19 |
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| C03 | 07 | X | ENG | @0 Markov decision @5 19 |
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| C03 | 07 | X | SPA | @0 Decisión Markov @5 19 |
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| C03 | 08 | X | FRE | @0 Processus Markov @5 23 |
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| C03 | 08 | X | ENG | @0 Markov process @5 23 |
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| C03 | 08 | X | SPA | @0 Proceso Markov @5 23 |
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| C03 | 09 | X | FRE | @0 Modélisation @5 24 |
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| C03 | 09 | X | ENG | @0 Modeling @5 24 |
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| C03 | 09 | X | SPA | @0 Modelización @5 24 |
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| C03 | 10 | X | FRE | @0 Méthode adaptative @5 25 |
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| C03 | 10 | X | ENG | @0 Adaptive method @5 25 |
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| C03 | 10 | X | SPA | @0 Método adaptativo @5 25 |
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| N21 | | | | @1 212 |
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| N44 | 01 | | | @1 OTO |
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| pR |
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Format Inist (serveur)
| NO : | PASCAL 06-0329056 INIST |
|---|
| FT : | Etude de différentes combinaisons de comportements adaptatives |
|---|
| AU : | BUFFET (Olivier); DUTECH (Alain); CHARPILLET (Francois); CHARPILLET (F.); GARCIA (F.); PERNY (Patrice); SIGAUD (Olivier) |
|---|
| AF : | LORIA - INRIA-Lorraine / Campus Scientifique - B.P. 239/54506 Vandoeuvre-lès-Nancy/France (1 aut., 2 aut., 3 aut.); National ICT Australia & The Autralian National Vniversity RSISE Building 115 - ANU//Canberra ACT 0200/Australie (1 aut.); LORIA-INRIA/Nancy/France (1 aut.); INRA-MIA/Toulouse/France (2 aut.); LIP6/Paris/France (3 aut., 4 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Revue d'intelligence artificielle; ISSN 0992-499X; France; Da. 2006; Vol. 20; No. 2-3; Pp. 311-343; Abs. anglais; Bibl. 1 p.3/4 |
|---|
| LA : | Français |
|---|
| EA : | This article focusses on the automated synthesis of agents in an uncertain environment, working in the setting of Reinforcement Learning, and more precisely of Partially Observable Markov Decision Processes. The agents (with no model of their environment and no short-term memory) are facing multiple motivations/goals simultaneously, a problem related to the field of Action Selection. We propose and evaluate various Action Selection architectures. They all combine already known basic behaviors in an adaptive manner, by learning the tuning of the combination, so as to maximize the agent's payoff. The logical continuation of this work is to automate the selection and design of the basic behaviors themselves. |
|---|
| CC : | 001D02C; 001D01A08 |
|---|
| FD : | Système incertain; Apprentissage renforcé; Long terme; Motivation; Agent intelligent; Observable; Décision Markov; Processus Markov; Modélisation; Méthode adaptative |
|---|
| ED : | Uncertain system; Reinforcement learning; Long term; Motivation; Intelligent agent; Observable; Markov decision; Markov process; Modeling; Adaptive method |
|---|
| SD : | Sistema incierto; Aprendizaje reforzado; Largo plazo; Motivación; Agente inteligente; Observable; Decisión Markov; Proceso Markov; Modelización; Método adaptativo |
|---|
| LO : | INIST-21320.354000142556580060 |
|---|
| ID : | 06-0329056 |
|---|
Links to Exploration step
Pascal:06-0329056
Le document en format XML
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</keywords><front><div type="abstract" xml:lang="en">This article focusses on the automated synthesis of agents in an uncertain environment, working in the setting of Reinforcement Learning, and more precisely of Partially Observable Markov Decision Processes. The agents (with no model of their environment and no short-term memory) are facing multiple motivations/goals simultaneously, a problem related to the field of Action Selection. We propose and evaluate various Action Selection architectures. They all combine already known basic behaviors in an adaptive manner, by learning the tuning of the combination, so as to maximize the agent's payoff. The logical continuation of this work is to automate the selection and design of the basic behaviors themselves.</div>
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<FT>Etude de différentes combinaisons de comportements adaptatives</FT>
<AU>BUFFET (Olivier); DUTECH (Alain); CHARPILLET (Francois); CHARPILLET (F.); GARCIA (F.); PERNY (Patrice); SIGAUD (Olivier)</AU>
<AF>LORIA - INRIA-Lorraine / Campus Scientifique - B.P. 239/54506 Vandoeuvre-lès-Nancy/France (1 aut., 2 aut., 3 aut.); National ICT Australia & The Autralian National Vniversity RSISE Building 115 - ANU//Canberra ACT 0200/Australie (1 aut.); LORIA-INRIA/Nancy/France (1 aut.); INRA-MIA/Toulouse/France (2 aut.); LIP6/Paris/France (3 aut., 4 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Revue d'intelligence artificielle; ISSN 0992-499X; France; Da. 2006; Vol. 20; No. 2-3; Pp. 311-343; Abs. anglais; Bibl. 1 p.3/4</SO>
<LA>Français</LA>
<EA>This article focusses on the automated synthesis of agents in an uncertain environment, working in the setting of Reinforcement Learning, and more precisely of Partially Observable Markov Decision Processes. The agents (with no model of their environment and no short-term memory) are facing multiple motivations/goals simultaneously, a problem related to the field of Action Selection. We propose and evaluate various Action Selection architectures. They all combine already known basic behaviors in an adaptive manner, by learning the tuning of the combination, so as to maximize the agent's payoff. The logical continuation of this work is to automate the selection and design of the basic behaviors themselves.</EA>
<CC>001D02C; 001D01A08</CC>
<FD>Système incertain; Apprentissage renforcé; Long terme; Motivation; Agent intelligent; Observable; Décision Markov; Processus Markov; Modélisation; Méthode adaptative</FD>
<ED>Uncertain system; Reinforcement learning; Long term; Motivation; Intelligent agent; Observable; Markov decision; Markov process; Modeling; Adaptive method</ED>
<SD>Sistema incierto; Aprendizaje reforzado; Largo plazo; Motivación; Agente inteligente; Observable; Decisión Markov; Proceso Markov; Modelización; Método adaptativo</SD>
<LO>INIST-21320.354000142556580060</LO>
<ID>06-0329056</ID>
</server>
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