Argument based machine learning
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TLDR
ABCN2, an argument-based extension of the CN2 rule learning algorithm is implemented, and its performance is analyzed to analyze its performance in comparison with the original CN2 algorithm.About:
This article is published in Artificial Intelligence.The article was published on 2007-07-01 and is currently open access. It has received 128 citations till now. The article focuses on the topics: Stability (learning theory) & Active learning (machine learning).read more
Citations
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Modern Applied Statistics With S
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
Journal ArticleDOI
Argumentation in artificial intelligence
TL;DR: A number of foundational contributions provided the basis for the formulation of argumentation models and their promotion in AI related settings and then a number of new themes that have emerged in recent years are considered, many of which provide the principal topics of the research presented in this volume.
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Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP
TL;DR: A definition of comprehensibility of hypotheses which can be estimated using human participant trials is provided and implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation.
Book ChapterDOI
The Added Value of Argumentation
Sanjay Modgil,Francesca Toni,Floris Bex,Ivan Bratko,Carlos Iván Chesñevar,Wolfgang Dvořák,Marcelo Alejandro Falappa,Xiuyi Fan,Sarah Alice Gaggl,Alejandro Javier García,María Paula González,Thomas F. Gordon,João Leite,Martin Možina,Chris Reed,Guillermo Ricardo Simari,Stefan Szeider,Paolo Torroni,Stefan Woltran +18 more
TL;DR: The value of argumentation in reaching agreements is discussed, based on its capability for dealing with conflicts and uncertainty, and a number of open challenges are identified if this potential is to be realised.
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A new approach for preference-based argumentation frameworks
Leila Amgoud,Srdjan Vesic +1 more
TL;DR: This paper proposes an approach that guarantees conflict-free extensions of argumentation framework and presents three dominance relations that generalize respectively stable, preferred and grounded semantics with preferences and retrieves the preferred sub-theories which were proposed in the context of handling inconsistency in weighted knowledge bases.
References
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