| 書(shū)目名稱 | Robot Learning | | 編輯 | Jonathan H. Connell,Sridhar Mahadevan | | 視頻video | http://file.papertrans.cn/832/831052/831052.mp4 | | 叢書(shū)名稱 | The Springer International Series in Engineering and Computer Science | | 圖書(shū)封面 |  | | 描述 | Building a robot that learns to perform a task has beenacknowledged as one of the major challenges facing artificialintelligence. Self-improving robots would relieve humans from much ofthe drudgery of programming and would potentially allow operation inenvironments that were changeable or only partially known. Progresstowards this goal would also make fundamental contributions toartificial intelligence by furthering our understanding of how tosuccessfully integrate disparate abilities such as perception,planning, learning and action..Although its roots can be traced back to the late fifties, the area ofrobot learning has lately seen a resurgence of interest. The flurry ofinterest in robot learning has partly been fueled by exciting new workin 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 researchprojects in robot learning being carried out at leading universitiesand research laboratories in the United States. The main researchdirections in robot learning covered in this book include:reinforcement learning, behavior-based architectures, ne | | 出版日期 | Book 1993 | | 關(guān)鍵詞 | Navigation; algorithms; artificial intelligence; artificial life; autonom; autonomous robot; genetic algor | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-3184-5 | | isbn_softcover | 978-1-4613-6396-5 | | isbn_ebook | 978-1-4615-3184-5Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Kluwer Academic Publishers 1993 |
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