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Artificial Intelligence
Article
License: Elsevier Non-Commercial
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Artificial Intelligence
Article . 2009
License: Elsevier Non-Commercial
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Artificial Intelligence
Article . 2009 . Peer-reviewed
License: Elsevier Non-Commercial
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The computational complexity of ideal semantics

Authors: Paul E. Dunne;

The computational complexity of ideal semantics

Abstract

AbstractWe analyse the computational complexity of the recently proposed ideal semantics within both abstract argumentation frameworks (afs) and assumption-based argumentation frameworks (abfs). It is shown that while typically less tractable than credulous admissibi-lity semantics, the natural decision problems arising with this extension-based model can, perhaps surprisingly, be decided more efficiently than sceptical preferred semantics. In particular the task of finding the unique ideal extension is easier than that of deciding if a given argument is accepted under the sceptical semantics. We provide efficient algorithmic approaches for the class of bipartite argumentation frameworks and, finally, present a number of technical results which offer strong indications that typical problems in ideal argumentation are complete for the class p∥C of languages decidable by polynomial time algorithms allowed to make non-adaptive queries to a C oracle, where C is an upper bound on the computational complexity of deciding credulous acceptance: C=np for afs and logic programming (lp) instantiations of abfs; C=Σ2p for abfs modelling default theories.

Related Organizations
Keywords

Computational complexity, Assumption-based argumentation, Artificial Intelligence, Abstract argumentation frameworks, Computational properties of argumentation

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    72
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
72
Top 10%
Top 10%
Top 10%
hybrid