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Argumentation in Artificial Intelligence

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Cover of 'Argumentation in Artificial Intelligence'

Table of Contents

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    Book Overview
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    Chapter 1 Argumentation Theory: A Very Short Introduction
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    Chapter 2 Semantics of Abstract Argument Systems
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    Chapter 3 Abstract Argumentation and Values
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    Chapter 4 Bipolar abstract argumentation systems
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    Chapter 5 Complexity of Abstract Argumentation
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    Chapter 6 Proof Theories and Algorithms for Abstract Argumentation Frameworks
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    Chapter 7 Argumentation Based on Classical Logic
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    Chapter 8 Argument-based Logic Programming
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    Chapter 9 A Recursive Semantics for Defeasible Reasoning
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    Chapter 10 Argumentation in Artificial Intelligence
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    Chapter 11 The Toulmin Argument Model in Artificial Intelligence
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    Chapter 12 Proof Burdens and Standards
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    Chapter 13 Dialogue Games for Agent Argumentation
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    Chapter 14 Models of Persuasion Dialogue
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    Chapter 15 Argumentation for Decision Making
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    Chapter 16 Argumentation and Game Theory
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    Chapter 17 Belief Revision and Argumentation Theory
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    Chapter 18 Argumentation in Legal Reasoning
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    Chapter 19 The Argument Interchange Format
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    Chapter 20 Empowering Recommendation Technologies Through Argumentation
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    Chapter 21 Arguing on the Semantic Grid
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    Chapter 22 Towards Probabilistic Argumentation
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    Chapter 23 Argument-Based Machine Learning
Attention for Chapter 15: Argumentation for Decision Making
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Citations

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Chapter title
Argumentation for Decision Making
Chapter number 15
Book title
Argumentation in Artificial Intelligence
Published in
ADS, May 2009
DOI 10.1007/978-0-387-98197-0_15
Book ISBNs
978-0-387-98196-3, 978-0-387-98197-0
Authors

Leila Amgoud

Editors

Guillermo Simari, Iyad Rahwan

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 6%
Austria 1 6%
Australia 1 6%
Unknown 13 81%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 31%
Student > Bachelor 2 13%
Researcher 2 13%
Student > Master 2 13%
Professor 1 6%
Other 2 13%
Unknown 2 13%
Readers by discipline Count As %
Computer Science 10 63%
Business, Management and Accounting 1 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Psychology 1 6%
Engineering 1 6%
Other 0 0%
Unknown 2 13%