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Titlebook: Symbolic and Quantitative Approaches to Reasoning with Uncertainty; 6th European Confere Salem Benferhat,Philippe Besnard Conference procee

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发表于 2025-3-21 19:11:39 | 显示全部楼层 |阅读模式
书目名称Symbolic and Quantitative Approaches to Reasoning with Uncertainty
副标题6th European Confere
编辑Salem Benferhat,Philippe Besnard
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Symbolic and Quantitative Approaches to Reasoning with Uncertainty; 6th European Confere Salem Benferhat,Philippe Besnard Conference procee
描述This book constitutes the refereed proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2001, held in Toulouse, France in September 2001..The 68 revised full papers presented together with three invited papers were carefully reviewed and selected from over a hundred submissions. The book offers topical sections on decision theory, partially observable Markov decision processes, decision-making, coherent probabilities, Bayesian networks, learning causal networks, graphical representation of uncertainty, imprecise probabilities, belief functions, fuzzy sets and rough sets, possibility theory, merging, belief revision and preferences, inconsistency handling, default logic, logic programming, etc.
出版日期Conference proceedings 2001
关键词Algorithmic Learning; Bayesian network; Causal Networks; Decision Making; Decision Theory; Fuzzy Logic; Fu
版次1
doihttps://doi.org/10.1007/3-540-44652-4
isbn_softcover978-3-540-42464-2
isbn_ebook978-3-540-44652-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2001
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Importance Sampling in Bayesian Networks Using Antithetic Variablesnce sampling is that during the simulation, configurations are obtained using antithetic variables (variables with negative correlation), achieving a reduction of the variance of the estimation. The performance of the new algorithm is tested by means of some experiments carried out over four large real-world networks.
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Planning with Uncertainty and Incomplete InformationWe consider the problem of planning in a general setting where actions can be deterministic or probabilistic, and their effects can be fully or partially observable. The task is to obtain a plan or closed-loop controller given a suitable description of the initial situation, actions, and goals.
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https://doi.org/10.1007/3-540-44652-4Algorithmic Learning; Bayesian network; Causal Networks; Decision Making; Decision Theory; Fuzzy Logic; Fu
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Symbolic and Quantitative Approaches to Reasoning with Uncertainty6th European Confere
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