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- Category : Secularism
- Languages : en
- Pages : 574
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This insightful and highly readable Advanced Introduction provides a succinct, yet comprehensive, overview of legal reasoning, covering both reasoning from canonical texts and legal decision-making in the absence of rules. Overall, it argues that there are only two methods by which judges decide legal disputes: deductive reasoning from rules and unconstrained moral, practical, and empirical reasoning.
This book constitutes the proceedings of the International Joint Conference on Rules and Reasoning, RuleML+RR 2018, held in Luxembourg during September 2018. This is the second conference of a new series, joining the efforts of two existing conference series, namely “RuleML” (International Web Rule Symposium) and “RR” (Web Reasoning and Rule Systems). The 10 full research papers presented together with 5 long technical communications and 7 short papers were carefully reviewed and selected from 33 submissions.
The book will focus on exploiting state of the art research in semantic web and web science. The rapidly evolving world-wide-web has led to revolutionary changes in the whole of society. The research and development of the semantic web covers a number of global standards of the web and cutting edge technologies, such as: linked data, social semantic web, semantic web search, smart data integration, semantic web mining and web scale computing. These proceedings are from the 6th Chinese Semantics Web Symposium.
Case-based reasoning (CBR) is an Artificial Intelligence (AI) technique to support the capability of reasoning and learning in advanced decision support systems. CBR exploits the specific knowledge collected on previously encountered and solved situations, which are known as cases. In this book, we have collected a selection of papers on very recent CBR applications. These, after an in-depth analysis of their specific application domain needs, propose proper methodological solutions and give encouraging evaluation results, which have in some cases led to the commercialization step. The collected contributions demonstrate the capability of CBR to solve or handle issues which would be too difficult to manage with other classical AI methods and techniques, such as rules or models. The heterogeneity of the involved application domains indicates the flexibility of CBR, and its applicability in all those fields where experiential knowledge is (readily) available.