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Titlebook: New Frontiers in Artificial Intelligence; JSAI-isAI 2010 Works Takashi Onada,Daisuke Bekki,Elin McCready Conference proceedings 2011 Spring

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书目名称New Frontiers in Artificial Intelligence
副标题JSAI-isAI 2010 Works
编辑Takashi Onada,Daisuke Bekki,Elin McCready
视频video
概述Fast-track conference proceedings.State-of-the-art research.Up-to-date results
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: New Frontiers in Artificial Intelligence; JSAI-isAI 2010 Works Takashi Onada,Daisuke Bekki,Elin McCready Conference proceedings 2011 Spring
描述This book constitutes thethoroughly refereed post-proceedings of four workshops held as satellite events of the JSAI International Symposia on Artificial Intelligence 2010, in Tokyo, Japan, in November 2010.The 28 revised full paperswith four papers for the following four workshops presented were carefully reviewed and selected from 70 papers. The papers are organized in sections Logic and Engineering of Natural Language Semantics (LENLS), Juris-Informatics (JURISIN), Advanced Methodologies for Bayesian Networks (AMBN), and Innovating Service Systems (ISS).
出版日期Conference proceedings 2011
关键词AI logics; legal information processing; machine learning; natural language processing; service science
版次1
doihttps://doi.org/10.1007/978-3-642-25655-4
isbn_softcover978-3-642-25654-7
isbn_ebook978-3-642-25655-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag GmbH Berlin Heidelberg 2011
The information of publication is updating

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PROLEG: An Implementation of the Presupposed Ultimate Fact Theory of Japanese Civil Code by PROLOG Togic programming is difficult for lawyers to understand. So, in this paper, we change knowledge representation of rules in the JUF theory in PROLEG so that we reflect lawyers’ reasoning using the idea of “openness” proposed by a judge who is a main investigator of the JUF theory.
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0302-9743 rganized in sections Logic and Engineering of Natural Language Semantics (LENLS), Juris-Informatics (JURISIN), Advanced Methodologies for Bayesian Networks (AMBN), and Innovating Service Systems (ISS).978-3-642-25654-7978-3-642-25655-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Searching Optimal Bayesian Network Structure on Constraint Search Space: Super-Structure Approachorithm can learn Bayesian networks with several hundreds of nodes when the degree of super-structure is around four. Numerical experiments indicate that constraint optimal search outperforms state-of-the-art heuristic algorithms in terms of accuracy, even if the super-structure is also learned by data.
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