6,601 research outputs found
Argumentation for machine learning: a survey
Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future
A QBF-based Formalization of Abstract Argumentation Semantics
Supported by the National Research Fund, Luxembourg (LAAMI project) and by the Engineering and Physical Sciences Research Council (EPSRC, UK), grant ref. EP/J012084/1 (SAsSY project).Peer reviewedPostprin
Some Supplementaries to The Counting Semantics for Abstract Argumentation
Dung's abstract argumentation framework consists of a set of interacting
arguments and a series of semantics for evaluating them. Those semantics
partition the powerset of the set of arguments into two classes: extensions and
non-extensions. In order to reason with a specific semantics, one needs to take
a credulous or skeptical approach, i.e. an argument is eventually accepted, if
it is accepted in one or all extensions, respectively. In our previous work
\cite{ref-pu2015counting}, we have proposed a novel semantics, called
\emph{counting semantics}, which allows for a more fine-grained assessment to
arguments by counting the number of their respective attackers and defenders
based on argument graph and argument game. In this paper, we continue our
previous work by presenting some supplementaries about how to choose the
damaging factor for the counting semantics, and what relationships with some
existing approaches, such as Dung's classical semantics, generic gradual
valuations. Lastly, an axiomatic perspective on the ranking semantics induced
by our counting semantics are presented.Comment: 8 pages, 3 figures, ICTAI 201
A Plausibility Semantics for Abstract Argumentation Frameworks
We propose and investigate a simple ranking-measure-based extension semantics
for abstract argumentation frameworks based on their generic instantiation by
default knowledge bases and the ranking construction semantics for default
reasoning. In this context, we consider the path from structured to logical to
shallow semantic instantiations. The resulting well-justified JZ-extension
semantics diverges from more traditional approaches.Comment: Proceedings of the 15th International Workshop on Non-Monotonic
Reasoning (NMR 2014). This is an improved and extended version of the
author's ECSQARU 2013 pape
On the equivalence between logic programming semantics and argumentation semantics
This work has been supported by the National Research Fund, Luxembourg (LAAMI project), by the Engineering and Physical Sciences Research Council (EPSRC, UK), grant Ref. EP/J012084/1 (SAsSy project), by CNPq (Universal 2012 ā Proc. 473110/2012-1), and by CNPq/CAPES (Casadinho/PROCAD 2011).Peer reviewedPreprin
A Labelling Framework for Probabilistic Argumentation
The combination of argumentation and probability paves the way to new
accounts of qualitative and quantitative uncertainty, thereby offering new
theoretical and applicative opportunities. Due to a variety of interests,
probabilistic argumentation is approached in the literature with different
frameworks, pertaining to structured and abstract argumentation, and with
respect to diverse types of uncertainty, in particular the uncertainty on the
credibility of the premises, the uncertainty about which arguments to consider,
and the uncertainty on the acceptance status of arguments or statements.
Towards a general framework for probabilistic argumentation, we investigate a
labelling-oriented framework encompassing a basic setting for rule-based
argumentation and its (semi-) abstract account, along with diverse types of
uncertainty. Our framework provides a systematic treatment of various kinds of
uncertainty and of their relationships and allows us to back or question
assertions from the literature
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