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Titlebook: Scalable Uncertainty Management; 11th International C Serafín Moral,Olivier Pivert,Nicolás Marín Conference proceedings 2017 Springer Inter

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发表于 2025-3-21 19:04:14 | 显示全部楼层 |阅读模式
书目名称Scalable Uncertainty Management
副标题11th International C
编辑Serafín Moral,Olivier Pivert,Nicolás Marín
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Scalable Uncertainty Management; 11th International C Serafín Moral,Olivier Pivert,Nicolás Marín Conference proceedings 2017 Springer Inter
描述This book constitutes the refereed proceedings of the 11th International Conference on Scalable Uncertainty Management, SUM 2017, which was held in Granada, Spain, in October 2017. . The 24 full and 6 short papers presented in this volume were carefully reviewed and selected from 35 submissions. The book also contains 3 invited papers. . Managing uncertainty and inconsistency has been extensively explored in Artificial Intelligence over a number of years. Now, with the advent of massive amounts of data and knowledge from distributed, heterogeneous, and potentially conflicting sources, there is interest in developing and applying formalisms for uncertainty and inconsistency in systems that need to better manage this data and knowledge. The International Conference on Scalable Uncertainty (SUM) aims to provide a forum for researchers who are working on uncertainty management, in different communities and with different uncertainty models, to meet and exchange ideas..
出版日期Conference proceedings 2017
关键词argumentation; artificial intelligence; Bayesian networks; data provenance; formal logic; fuzzy logic; heu
版次1
doihttps://doi.org/10.1007/978-3-319-67582-4
isbn_softcover978-3-319-67581-7
isbn_ebook978-3-319-67582-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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https://doi.org/10.1007/978-3-319-67582-4argumentation; artificial intelligence; Bayesian networks; data provenance; formal logic; fuzzy logic; heu
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Maximum Likelihood Estimation and Coarse Dataxpressed in terms of subsets of the sample space. We consider situations where the coarsening process is stochastic, and illustrate with examples how ignoring this process may produce misleading estimations.
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Reasons and Means to Model Preferences as Incompletenary relation. Much has been written however on different models of preferences. We review some of the reasons that have been put forward to justify more complex modeling, and review some of the techniques that have been proposed to obtain models of such preferences.
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Measuring Disagreement in Argumentation Graphson of . as well as a set of principles that such a measure should satisfy. We propose some intuitive measures and show that they fail to satisfy some of the principles. Then, we come up with a more discriminating measure which satisfies them all. Finally, we relate some measures to those quantifying inconsistency in knowledge bases.
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978-3-319-67581-7Springer International Publishing AG 2017
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Scalable Uncertainty Management978-3-319-67582-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
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