Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.
Описание: This book introduces a new paradigm called ‘Optimization in Changeable Spaces’ (OCS) as a useful tool for decision making and problem solving. It illustrates how OCS incorporates, searches, and constructively restructures the parameters, tangible and intangible, involved in the process of decision making. The book elaborates on OCS problems that can be modeled and solved effectively by using the concepts of competence set analysis, Habitual Domain (HD) and the mental operators called the 7-8-9 principles of deep knowledge of HD. In addition, new concepts of covering and discovering processes are proposed and formulated as mathematical tools to solve OCS problems. The book also includes reformulations of a number of illustrative real-life challenging problems that cannot be solved by traditional optimization techniques into OCS problems, and details how they can be addressed. Beyond that, it also includes perspectives related to innovation dynamics, management, artificial intelligence, artificial and e-economics, scientific discovery and knowledge extraction. This book will be of interest to managers of businesses and institutions, policy makers, and educators and students of decision making and behavior in DBA and/or MBA.
Автор: Matthew Taylor Название: Transfer in Reinforcement Learning Domains ISBN: 3642101860 ISBN-13(EAN): 9783642101861 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research.
Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.
Автор: Harald Sack; Eva Blomqvist; Mathieu d`Aquin; Chiar Название: The Semantic Web. Latest Advances and New Domains ISBN: 3319341286 ISBN-13(EAN): 9783319341286 Издательство: Springer Рейтинг: Цена: 13416.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The 47 revised full papers presented together with three invited talks were carefully reviewed and selected from 204 submissions. In addition, the PhD Symposium program included 10 contributions, selected out of 21 submissions. The core tracks of the research conference were complemented with new tracks focusing on linked data;
Автор: Matthew Taylor Название: Transfer in Reinforcement Learning Domains ISBN: 3642018815 ISBN-13(EAN): 9783642018817 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reinforcement Learning Background.- Related Work.- Empirical Domains.- Value Function Transfer via Inter-Task Mappings.- Extending Transfer via Inter-Task Mappings.- Transfer between Different Reinforcement Learning Methods.- Learning Inter-Task Mappings.- Conclusion and Future Work.
Автор: Alfio Gliozzo; Carlo Strapparava Название: Semantic Domains in Computational Linguistics ISBN: 3642425860 ISBN-13(EAN): 9783642425868 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An ideal text for researchers and graduate students, this comprehensive volume covers semantic domains and domain models, as well as ways of applying the technique to text categorization, word sense disambiguation, and cross-language text categorization.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
