2,249 research outputs found
Abstract Argumentation / Persuasion / Dynamics
The act of persuasion, a key component in rhetoric argumentation, may be
viewed as a dynamics modifier. We extend Dung's frameworks with acts of
persuasion among agents, and consider interactions among attack, persuasion and
defence that have been largely unheeded so far. We characterise basic notions
of admissibilities in this framework, and show a way of enriching them through,
effectively, CTL (computation tree logic) encoding, which also permits
importation of the theoretical results known to the logic into our
argumentation frameworks. Our aim is to complement the growing interest in
coordination of static and dynamic argumentation.Comment: Arisaka R., Satoh K. (2018) Abstract Argumentation / Persuasion /
Dynamics. In: Miller T., Oren N., Sakurai Y., Noda I., Savarimuthu B., Cao
Son T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems.
PRIMA 2018. Lecture Notes in Computer Science, vol 11224. Springer, Cha
Stratified Labelings for Abstract Argumentation
We introduce stratified labelings as a novel semantical approach to abstract
argumentation frameworks. Compared to standard labelings, stratified labelings
provide a more fine-grained assessment of the controversiality of arguments
using ranks instead of the usual labels in, out, and undecided. We relate the
framework of stratified labelings to conditional logic and, in particular, to
the System Z ranking functions
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
Counting Complexity for Reasoning in Abstract Argumentation
In this paper, we consider counting and projected model counting of
extensions in abstract argumentation for various semantics. When asking for
projected counts we are interested in counting the number of extensions of a
given argumentation framework while multiple extensions that are identical when
restricted to the projected arguments count as only one projected extension. We
establish classical complexity results and parameterized complexity results
when the problems are parameterized by treewidth of the undirected
argumentation graph. To obtain upper bounds for counting projected extensions,
we introduce novel algorithms that exploit small treewidth of the undirected
argumentation graph of the input instance by dynamic programming (DP). Our
algorithms run in time double or triple exponential in the treewidth depending
on the considered semantics. Finally, we take the exponential time hypothesis
(ETH) into account and establish lower bounds of bounded treewidth algorithms
for counting extensions and projected extension.Comment: Extended version of a paper published at AAAI-1
Exploiting Parallelism for Hard Problems in Abstract Argumentation
Abstract argumentation framework (AF) is a unifying framework able to encompass a variety of nonmonotonic reasoning approaches, logic programming and computational argumentation. Yet, efficient approaches for most of the decision and enumeration problems associated to AF s are missing, thus potentially limiting the efficacy of argumentation-based approaches in real domains. In this paper, we present an algorithm for enumerating the preferred extensions of abstract argumentation frameworks which exploits parallel computation. To this purpose, the SCC-recursive semantics definition schema is adopted, where extensions are defined at the level of specific sub-frameworks. The algorithm shows significant performance improvements in large frameworks, in terms of number of solutions found and speedup
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