| Titre : |
Robot learning |
| Type de document : |
texte imprimé |
| Auteurs : |
Jonathan Hudson Connell, Éditeur scientifique ; Sridhar Mahadevan, Éditeur scientifique |
| Editeur : |
Boston : Kluwer academic publishers |
| Année de publication : |
1993 |
| Collection : |
The kluwer international series in engineering and computer science |
| Sous-collection : |
Knowledge representation, learning and expert systems num. 233 |
| Importance : |
XII-240 p. |
| Présentation : |
ill. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-0-7923-9365-8 |
| Note générale : |
Bibliogr p. 215-235. Index |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Robots -- Control systems
Intelligence artificielle
Machine learning
Robots -- Systèmes de commande
Apprentissage automatique |
| Index. décimale : |
62-52 Machine et processus conduits ou contrôlés automatiquement |
| Résumé : |
Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action.
Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration. |
| Note de contenu : |
Contents:
*Introduction to robot learning .
*Knowledge-based training of artificial neural networks for autonomous robot driving.
*Learing multiple goal behavior via task decomposition and dynamic policy merging .
*Memory-based reinforcement learning: converging with less data and less real time.
*Rapide task learning for real robots.
... |
Robot learning [texte imprimé] / Jonathan Hudson Connell, Éditeur scientifique ; Sridhar Mahadevan, Éditeur scientifique . - Boston : Kluwer academic publishers, 1993 . - XII-240 p. : ill. ; 24 cm. - ( The kluwer international series in engineering and computer science. Knowledge representation, learning and expert systems; 233) . ISBN : 978-0-7923-9365-8 Bibliogr p. 215-235. Index Langues : Anglais ( eng)
| Mots-clés : |
Robots -- Control systems
Intelligence artificielle
Machine learning
Robots -- Systèmes de commande
Apprentissage automatique |
| Index. décimale : |
62-52 Machine et processus conduits ou contrôlés automatiquement |
| Résumé : |
Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action.
Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration. |
| Note de contenu : |
Contents:
*Introduction to robot learning .
*Knowledge-based training of artificial neural networks for autonomous robot driving.
*Learing multiple goal behavior via task decomposition and dynamic policy merging .
*Memory-based reinforcement learning: converging with less data and less real time.
*Rapide task learning for real robots.
... |
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