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Titlebook: Reasoning with Uncertainty in Robotics; International Worksh Leo Dorst,Michiel Lambalgen,Frans Voorbraak Conference proceedings 1996 Spring

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書目名稱Reasoning with Uncertainty in Robotics
副標(biāo)題International Worksh
編輯Leo Dorst,Michiel Lambalgen,Frans Voorbraak
視頻videohttp://file.papertrans.cn/823/822506/822506.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Reasoning with Uncertainty in Robotics; International Worksh Leo Dorst,Michiel Lambalgen,Frans Voorbraak Conference proceedings 1996 Spring
描述This book presents the refereed proceedings of the International Workshop on Reasoning with Uncertainty in Robotics, RUR‘95, held in Amsterdam, The Netherlands, in December 1995..The book contains 13 revised full papers carefully selected for presentation during the workshop together with six invited papers. Also included are two comprehensive tutorial texts and an introduction by the volume editors. Thus the book is both a competent state-of-the-art report on current research and development and a valuable survey and introduction for researchers entering the area or professionals interested in the application of up-to-date techniques.
出版日期Conference proceedings 1996
關(guān)鍵詞Autonome Mobile Agenten; Autonomous Mobile Agents; Bayes-Entscheidungstheorie; Bayesian Decision Theory
版次1
doihttps://doi.org/10.1007/BFb0013951
isbn_softcover978-3-540-61376-3
isbn_ebook978-3-540-68506-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 1996
The information of publication is updating

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沙發(fā)
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板凳
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Reasoning with Uncertainty in Robotics978-3-540-68506-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
5#
發(fā)表于 2025-3-22 12:26:12 | 只看該作者
Reasoning with uncertainty in AI,This paper provides an introduction to the field of reasoning with uncertainty in Artificial Intelligence (AI), with an emphasis on reasoning with numeric uncertainty. The considered formalisms are Probability Theory and some of its generalizations, the Certainty Factor Model, Dempster-Shafer Theory, and Probabilistic Networks.
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發(fā)表于 2025-3-22 13:30:13 | 只看該作者
7#
發(fā)表于 2025-3-22 19:51:35 | 只看該作者
An evidential approach to probabilistic map-building,’ into the map using the Dempster-Shafer inference rule. This evidential approach with its multi-valued hypotheses allows quantitative analysis of the quality of the data. The map building system is experimentally evaluated using sonar data from real environments.
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發(fā)表于 2025-3-23 04:16:00 | 只看該作者
Neural network applications in sensor fusion for an autonomous mobile robot,d how to represent the sensor‘s error characteristics (the sensor model) in such a representation. For both these problems, solutions are suggested by the application of neural network theory, and it is argued that these neural networks are the best available alternatives.
10#
發(fā)表于 2025-3-23 07:04:03 | 只看該作者
Uncertainty treatment in a surface filling mobile robot, the motion system. Robustness of the system is augmented both by considering uncertainty inside each module and by an intrinsic co-operation between modules. A high level of performance has been reached at low hardware requirements.
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