<div class="csl-bib-body">
<div class="csl-entry">Dvořák, W., Rapberger, A., & Woltran, S. (2020). Strong Equivalence for Argumentation Frameworks with Collective Attacks. In <i>ECAI 2020 - 24th European Conference on Artificial Intelligence</i> (pp. 721–728). IOS Press. https://doi.org/10.3233/FAIA200159</div>
</div>
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dc.identifier.isbn
9783030301798
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dc.identifier.isbn
9783030301781
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/58263
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dc.description.abstract
Dung´s abstract argumentation frameworks (AFs) are a popular conceptual tool to define semantics for advanced argumentation formalisms. Hereby, arguments representing a possible inference of a claim are constructed and an attack relation between arguments indicates certain conflicts between the claim of one argument and the inference of another. Based on this abstract model, sets of jointly acceptable arguments are then gathered and finally interpreted in terms of their claims. Argumentation formalisms following this type of instantiating Dung AFs naturally produce several arguments with the same claim. This causes several issues and challenges for argumentation systems: on the one hand, the relation between claims remains implicit and, on the other hand, determining the acceptance of claims requires additional computations on top of argument acceptance. An instantiation that avoids this situation could provide additional insights and advantages, thus complementing the standard instantiation process via Dung AFs. Consequently, the research question we tackle is as follows: Can one combine different arguments sharing the same claim to a single abstract argument without affecting the overall results (and which abstract formalisms can serve such a purpose)? As a main result we show that a certain class of frameworks, where arguments with the same claim have the same outgoing attacks, can be equivalently (for all standard semantics) represented as argumentation frameworks with collective attacks where each claim occurs in exactly one argument. We further identify a class of frameworks where one even obtains an equivalent Dung AF with just one argument per claim.
en
dc.language.iso
en
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dc.relation.ispartofseries
Frontiers in Artificial Intelligence and Applications
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dc.title
Strong Equivalence for Argumentation Frameworks with Collective Attacks
en
dc.type
Konferenzbeitrag
de
dc.type
Inproceedings
en
dc.relation.isbn
978-1-64368-101-6
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dc.description.startpage
721
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dc.description.endpage
728
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
ECAI 2020 - 24th European Conference on Artificial Intelligence
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tuw.container.volume
325
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tuw.peerreviewed
true
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tuw.relation.publisher
IOS Press
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tuw.researchTopic.id
I1
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tuw.researchTopic.name
Logic and Computation
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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tuw.publisher.doi
10.3233/FAIA200159
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dc.description.numberOfPages
8
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tuw.author.orcid
0000-0003-0355-3535
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tuw.author.orcid
0000-0003-1594-8972
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tuw.event.name
ECAI 2020
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tuw.event.startdate
29-08-2020
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tuw.event.enddate
05-09-2020
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tuw.event.online
Online
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tuw.event.type
Event for scientific audience
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tuw.event.place
Santiago de Compostela
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tuw.event.country
ES
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tuw.event.presenter
Dvořák, Wolfgang
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tuw.presentation.online
Online
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.facultyfocus
Logic and Computation (LC)
de
wb.facultyfocus
Logic and Computation (LC)
en
wb.facultyfocus.faculty
E180
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wb.presentation.type
science to science/art to art
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item.cerifentitytype
Publications
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item.cerifentitytype
Publications
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item.fulltext
no Fulltext
-
item.grantfulltext
none
-
item.languageiso639-1
en
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item.openairetype
Konferenzbeitrag
-
item.openairetype
Inproceedings
-
item.openairecristype
http://purl.org/coar/resource_type/c_18cf
-
item.openairecristype
http://purl.org/coar/resource_type/c_18cf
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence