Efficient computation of argumentation semantics [recurso electrónico] / Beishui Liao.
Tipo de material: TextoSeries Intelligent systems seriesDetalles de publicación: Oxford : Academic Press, 2014Descripción: 1 online resource (149 pages)Tipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9780124104068; 0124104061; 9780124104518; 0124104517Tema(s): Semantic computing | Artificial intelligence | Artificial intelligence | Semantic computing | Semantics | COMPUTERS -- General | Artificial intelligence | Semantic computingGénero/Forma: Electronic books.Formatos físicos adicionales: Print version:: Efficient Computation of Argumentation Semantics.Clasificación CDD: 006.3/5 Clasificación LoC:P98 | .E384 2014Recursos en línea: Libro electrónico ScienceDirectTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
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Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | P98 .E384 2014 (Browse shelf(Abre debajo)) | 1 | No para préstamo | 380112-2001 |
Print version record.
Half Title; Editorial Page; Title Page; Copyright; Contents; Preface; 1 Introduction; 1.1 Background; 1.2 The Notion of Argumentation; 1.3 Motivations of this Book; 1.4 The Structure of this Book; References; 2 Semantics of Argumentation; 2.1 Introduction; 2.2 Abstract Argumentation Frameworks; 2.3 Argumentation Semantics; 2.3.1 Extension-based Approach; 2.3.1.1 Admissible Extension; 2.3.1.2 Complete Extension; 2.3.1.3 Grounded Extension and Preferred Extension; 2.3.1.4 Stable Extension and Semi-Stable Extension; 2.3.1.5 Ideal Extension and Eager Extension; 2.3.2 Labelling-based Approach.
2.3.2.1 Admissible Labelling2.3.2.2 Complete Labelling; 2.3.2.3 Grounded Labelling and Preferred Labelling; 2.3.2.4 Stable Labelling and Semi-Stable Labelling; 2.3.2.5 Ideal Labelling and Eager Labelling; 2.3.3 Relations Between the Two Approaches; 2.3.4 Relations Between Different Semantics; 2.3.5 Status of Arguments; 2.4 Conclusions; References; 3 Existing Approaches for Computing Argumentation Semantics; 3.1 Introduction; 3.2 Approaches Based on Answer Set Programming; 3.2.1 Answer Set Programming; 3.2.1.1 Syntax; 3.2.1.2 Answer Set Semantics; 3.2.2 ASP for Argumentation.
3.3 Labelling-Based Algorithms3.3.1 The Computation of Grounded Labellings; 3.3.2 The Computation of Preferred Labellings; 3.3.2.1 Generating Admissible Labellings; 3.3.2.2 Generating Preferred Labellings; 3.4 Conclusions; References; 4 Sub-Frameworks and Local Semantics; 4.1 Introduction; 4.2 Notion of Sub-Frameworks; 4.2.1 Informal Idea; 4.2.2 Formal Definition; 4.2.3 Dependence Relation Between Different Sub-Frameworks; 4.3 Semantics of Sub-Frameworks; 4.3.1 Labellings of a Conditioned Sub-Framework; 4.3.2 Extensions of a Conditioned Sub-Framework.
4.4 Computation of the Semantics of a Sub-Framework4.5 Conclusions; References; 5 Relations between Global Semantics and Local Semantics; 5.1 Introduction; 5.2 Mapping Global Semantics to Local Semantics; 5.3 Mapping Local Semantics to Global Semantics; 5.3.1 Combining Extensions of Two Unconditioned Sub-Frameworks; 5.3.2 Combining Extensions of a Conditioned Sub-Framework and Those of an Unconditioned Sub-Framework; 5.3.3 Combining Labellings of Two Conditioned Sub-Frameworks; 5.4 Conclusions; References; 6 An Approach for Static Argumentation Frameworks; 6.1 Introduction.
6.2 Decomposing an Argumentation Framework: A Layered Approach6.2.1 Strongly Connected Components of an Argumentation Framework; 6.2.2 A Decomposition Approach Based on SCCs; 6.3 An Incremental Approach to Compute Argumentation Semantics; 6.3.1 The Computation of Layer i (); 6.3.1.1 Constructing Partially Labelled Sub-Frameworks in Layer i (); 6.3.1.2 Computing the Labellings of Each Sub-Framework in Layer i (); 6.3.1.3 Horizontally Combining the Labellings of Layer i (); 6.3.1.4 Vertically Combining the Labellings of Layers from 0 to i ().
Includes index.
Efficient Computation of Argumentation Semantics addresses argumentation semantics and systems, introducing readers to cutting-edge decomposition methods that drive increasingly efficient logic computation in AI and intelligent systems. Such complex and distributed systems are increasingly used in the automation and transportation systems field, and particularly autonomous systems, as well as more generic intelligent computation research. The Series in Intelligent Systems publishes titles that cover state-of-the-art knowledge and the latest advances in research and development.
Includes bibliographical references and index.
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