Titlebook: A Guided Tour of Artificial Intelligence Research; Volume I: Knowledge Pierre Marquis,Odile Papini,Henri Prade Book 2020 Springer Nature S

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Algorithmic Aspects of Flows in Networks this volume. The theory of belief functions and the theory of imprecise probabilities are presented. The latter setting is mathematically more general than the former, and both include probability theory and quantitative possibility theory as particular cases. Their respective knowledge representat

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圖畫(huà)文字

https://doi.org/10.1007/978-3-540-77871-4e representation and reasoning formalisms that put ontologies at the core of any knowledge base are presented, namely: description logics, conceptual graphs and existential rules. We present the main knowledge constructs and dialects of these families, as well as the main reasoning problems with the

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Topology and Routing in Sensor Networks,les with discrete value domains). In the first part of the chapter, we present the problem in its full generality. A large part of these languages are said to be ., because they work by expressing elementary preferences in a local way, using structural independence properties that are represented un

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https://doi.org/10.1007/978-3-540-92862-1d optionality. Deontic logic is at the origin of normative systems which are used to model obligations, prohibitions and sanctions in organizations. In this chapter, we will first present standard deontic logic, then we will analyze its drawbacks. A synthesis of some problems tackled in normative sy

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Valentin Polishchuk,Jukka Suomelafferent perspectives ranging from the use of causal links in diagnosis or in reasoning about action to the ascription of causality relations and the assessment of responsibility. In the last two decades, some formal models of causality, such as those proposed by Pearl and Halpern, have been much inf

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Lecture Notes in Computer Sciencecase (where a case represents a problem-solving episode) similar to the problem to be solved and to adapt it to solve this problem. Section?. is devoted to analogical reasoning and to recent developments based on analogical proportion. Interpolative reasoning, presented in Sect.?. in the formal sett

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Magnús M. Halldórsson,Takeshi Tokuyamas: sample complexity and runtime complexity. This chapter is a short introduction to this important area of research, geared toward the reader interested in developing learning algorithms for AI models. We first provide the formal background about statistical learning problems, captured by three bas

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Wie Daten zu Verzerrungen führen k?nnenm giving up any piece of information (by contrast with revision, as discussed in chapter?“Main Issues in Belief Revision, Belief Merging and Information Fusion” of this volume). The baseline is to get the best, resorting to various possibilities, from the available information in order to reason in