Titlebook: Conditionals in Nonmonotonic Reasoning and Belief Revision; Considering Conditio Gabriele Kern-Isberner Book 2001 Springer-Verlag Berlin He

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Qing Wang,Hong Chen,Jinfeng Li,Dehai Zhuthrow light on this question from two different sides..The first part of this chapter (cf. also [KI98b]) investigates how infering at optimum entropy fits the formal framework for nonmonotonic reasoning of [Mak94]. In particular, we show that any inference operation based on ME-reasoning is . and sa

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https://doi.org/10.1007/978-3-662-50544-1s of reasoning in response to incoming information (cf. Chapter 2, in particular, Section 2.3). As we already pointed out, the crucial difference between both areas is the role of the knowledge base which is only implicit in nonmonotonic reasoning, but explicit and in fact in the focus of interest i

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Yuyong He,Zhigeng Pan,Haiying Zhaoil the formal properties of conditionals, how to represent them appropriately and how to handle them under change of beliefs. Solving these problems is a necessary prerequisite to arrive at a satisfactory representation and processing of knowledge. When designing an expert system, however, at first

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/235221.jpg

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Qing Wang,Hong Chen,Yan Liu,Dehai ZhuConditionals are closely connected with reasoning - typically, they suggest plausible, yet often defeasible conclusions from what is evident. Therefore, studying conditionals means primarily to overcome the strict framework of classical logic and to enter into the world of defeasible reasoning, nonmonotonic logics and uncertain knowledge.

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Introduction,Conditionals are closely connected with reasoning - typically, they suggest plausible, yet often defeasible conclusions from what is evident. Therefore, studying conditionals means primarily to overcome the strict framework of classical logic and to enter into the world of defeasible reasoning, nonmonotonic logics and uncertain knowledge.

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Algorithms and Implementations,In this chapter, we present a selection of various computational approaches to quantified uncertain reasoning and probabilistic knowledge discovery.