Robot Learning:
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 p...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1993
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| Schriftenreihe: | The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems
233 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (XIII, 240 p) |
| ISBN: | 9781461531845 |
| DOI: | 10.1007/978-1-4615-3184-5 |
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| 520 | |a 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 | ||
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| 650 | 4 | |a Robotics and Automation | |
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| 650 | 4 | |a Engineering | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Control engineering | |
| 650 | 4 | |a Robotics | |
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Datensatz im Suchindex
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| author2 | Connell, Jonathan H. Mahadevan, Sridhar |
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| dewey-full | 629.892 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.892 |
| dewey-search | 629.892 |
| dewey-sort | 3629.892 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| doi_str_mv | 10.1007/978-1-4615-3184-5 |
| format | Electronic eBook |
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| id | DE-604.BV045187305 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:59Z |
| institution | BVB |
| isbn | 9781461531845 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030576483 |
| oclc_num | 1053842788 |
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| physical | 1 Online-Ressource (XIII, 240 p) |
| psigel | ZDB-2-ENG ZDB-2-ENG_Archiv ZDB-2-ENG ZDB-2-ENG_Archiv |
| publishDate | 1993 |
| publishDateSearch | 1993 |
| publishDateSort | 1993 |
| publisher | Springer US |
| record_format | marc |
| series2 | The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems |
| spelling | Robot Learning edited by Jonathan H. Connell, Sridhar Mahadevan Boston, MA Springer US 1993 1 Online-Ressource (XIII, 240 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems 233 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 Engineering Robotics and Automation Control, Robotics, Mechatronics Artificial Intelligence (incl. Robotics) Artificial intelligence Control engineering Robotics Mechatronics Automation Connell, Jonathan H. edt Mahadevan, Sridhar edt Erscheint auch als Druck-Ausgabe 9781461363965 https://doi.org/10.1007/978-1-4615-3184-5 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Robot Learning Engineering Robotics and Automation Control, Robotics, Mechatronics Artificial Intelligence (incl. Robotics) Artificial intelligence Control engineering Robotics Mechatronics Automation |
| title | Robot Learning |
| title_auth | Robot Learning |
| title_exact_search | Robot Learning |
| title_full | Robot Learning edited by Jonathan H. Connell, Sridhar Mahadevan |
| title_fullStr | Robot Learning edited by Jonathan H. Connell, Sridhar Mahadevan |
| title_full_unstemmed | Robot Learning edited by Jonathan H. Connell, Sridhar Mahadevan |
| title_short | Robot Learning |
| title_sort | robot learning |
| topic | Engineering Robotics and Automation Control, Robotics, Mechatronics Artificial Intelligence (incl. Robotics) Artificial intelligence Control engineering Robotics Mechatronics Automation |
| topic_facet | Engineering Robotics and Automation Control, Robotics, Mechatronics Artificial Intelligence (incl. Robotics) Artificial intelligence Control engineering Robotics Mechatronics Automation |
| url | https://doi.org/10.1007/978-1-4615-3184-5 |
| work_keys_str_mv | AT connelljonathanh robotlearning AT mahadevansridhar robotlearning |