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Titlebook: Decision Making under Constraints; Martine Ceberio,Vladik Kreinovich Book 2020 Springer Nature Switzerland AG 2020 Computational Intellige

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发表于 2025-3-21 19:18:18 | 显示全部楼层 |阅读模式
书目名称Decision Making under Constraints
编辑Martine Ceberio,Vladik Kreinovich
视频video
概述Presents extended versions of selected papers from the annual International Workshops on Constraint Programming and Decision Making from 2016 to 2018.Addresses all stages of decision-making under cons
丛书名称Studies in Systems, Decision and Control
图书封面Titlebook: Decision Making under Constraints;  Martine Ceberio,Vladik Kreinovich Book 2020 Springer Nature Switzerland AG 2020 Computational Intellige
描述.This book presents extended versions of selected papers from the annual International Workshops on Constraint Programming and Decision Making from 2016 to 2018. The papers address all stages of decision-making under constraints: (1) precisely formulating the problem of multi-criteria decision-making; (2) determining when the corresponding decision problem is algorithmically solvable; (3) finding the corresponding algorithms and making these algorithms as efficient as possible; and (4) taking into account interval, probabilistic, and fuzzy uncertainty inherent in the corresponding decision-making problems. In many application areas, it is necessary to make effective decisions under constraints, and there are several area-specific techniques for such decision problems. However, because they are area-specific, it is not easy to apply these techniques in other application areas. As such, the annual International Workshops on Constraint Programming and Decision Making focus on cross-fertilization between different areas, attracting researchers and practitioners from around the globe. The book includes numerous papers describing applications, in particular, applications to engineering,
出版日期Book 2020
关键词Computational Intelligence; Constraint Programming; Decision Making; Decision Making Under Constraints;
版次1
doihttps://doi.org/10.1007/978-3-030-40814-5
isbn_softcover978-3-030-40816-9
isbn_ebook978-3-030-40814-5Series ISSN 2198-4182 Series E-ISSN 2198-4190
issn_series 2198-4182
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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发表于 2025-3-21 21:50:44 | 显示全部楼层
How Neural Networks (NN) Can (Hopefully) Learn Faster by Taking into Account Known Constraints,. The major reason why learning in neural networks is slow is that neural networks are currently unable to take prior knowledge into account. As a result, they simply ignore this knowledge and simulate learning “from scratch”. In this paper, we show how neural networks can take prior knowledge into account and thus, hopefully, learn faster.
发表于 2025-3-22 01:39:36 | 显示全部楼层
Italian Folk Multiplication Algorithm Is Indeed Better: It Is More Parallelizable,gorithms traditionally used by their ethnic group—or simply to their sense of curiosity. Somewhat surprisingly, we show that one of these algorithms—a traditional Italian multiplication algorithm—is actually in some reasonable sense better than the algorithm that we all normally use—namely, it is easier to parallelize.
发表于 2025-3-22 06:37:50 | 显示全部楼层
Reverse Mathematics Is Computable for Interval Computations,y the given system. In this paper, we show that while we cannot always exactly describe this set, it is possible to have a general algorithm that, given ., provides an .-approximation to the desired solution set.
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Why Decimal System and Binary System Are the Most Widely Used: A Possible Explanation,, its sides are almost exactly 7/10, and when we want to construct a cube of half volume its sides are almost exactly 8/10. In this paper, we show that 2, 4, and 10 are the only numbers with this property—at least among the first billion numbers. This may be a possible explanation of why decimal and binary systems are the most widely used.
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发表于 2025-3-23 02:21:53 | 显示全部楼层
Database and Expert Systems Applicationsy changing circumstances is often not a good idea, the existence of a pre-computed original plan enables us to produce an almost-optimal strategy—a strategy that would have been computationally difficult to produce on a short notice without the pre-existing plan.
发表于 2025-3-23 06:13:07 | 显示全部楼层
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