30,282 research outputs found
Modeling time and valuation in structured argumentation frameworks
Temporal Argumentation Frameworks (TAF) represent a recent extension of Dung's abstract argumentation frameworks that consider the temporal availability of arguments. In a TAF, arguments are valid during specific time intervals, called availability intervals, while the attack relation of the framework remains static and permanent in time; thus, in general, when identifying the set of acceptable arguments, the outcome associated with a TAF will vary in time. We introduce an extension of TAF, called Extended Temporal Argumentation Framework (E-TAF), adding the capability of modeling the temporal availability of attacks among arguments, thus modeling special features of arguments varying over time and the possibility that attacks are only available in a given time interval. E-TAF will be enriched by considering Structured Abstract Argumentation, using Dynamic Argumentation Frameworks. The resulting framework, E-TAFā, provides a suitable model for different time-dependent issues satisfying properties and equivalence results that permit to contrast the expressivity of E-TAF and E-TAFā with argumentation based on abstract frameworks. Thus, the main contribution here is to provide an enhanced framework for modeling special features of argumentation varying over time, which are relevant in many real-world situations. The proposal aims at advancing in the integration of time and valuation in the context of argumentation systems as well.Fil: Budan, Maximiliano Celmo David. Universidad Nacional del Sur. Departamento de Ciencias e IngenierĆa de la ComputaciĆ³n; Argentina. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y TecnologĆas. Departamento de MatemĆ”tica; Argentina. Consejo Nacional de Investigaciones CientĆficas y TĆ©cnicas. Centro CientĆfico TecnolĆ³gico Conicet - BahĆa Blanca; ArgentinaFil: Gomez Lucero, Mauro Javier. Consejo Nacional de Investigaciones CientĆficas y TĆ©cnicas. Centro CientĆfico TecnolĆ³gico Conicet - BahĆa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierĆa de la ComputaciĆ³n; ArgentinaFil: ChesƱevar, Carlos IvĆ”n. Consejo Nacional de Investigaciones CientĆficas y TĆ©cnicas. Centro CientĆfico TecnolĆ³gico Conicet - BahĆa Blanca; Argentina. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y TecnologĆas. Departamento de MatemĆ”tica; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierĆa de la ComputaciĆ³n; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones CientĆficas y TĆ©cnicas. Centro CientĆfico TecnolĆ³gico Conicet - BahĆa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierĆa de la ComputaciĆ³n; Argentin
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
Reasoning about Action: An Argumentation - Theoretic Approach
We present a uniform non-monotonic solution to the problems of reasoning
about action on the basis of an argumentation-theoretic approach. Our theory is
provably correct relative to a sensible minimisation policy introduced on top
of a temporal propositional logic. Sophisticated problem domains can be
formalised in our framework. As much attention of researchers in the field has
been paid to the traditional and basic problems in reasoning about actions such
as the frame, the qualification and the ramification problems, approaches to
these problems within our formalisation lie at heart of the expositions
presented in this paper
Explanation for case-based reasoning via abstract argumentation
Case-based reasoning (CBR) is extensively used in AI in support of several applications, to assess a new situation (or case) by recollecting past situations (or cases) and employing the ones most similar to the new situation to give the assessment. In this paper we study properties of a recently proposed method for CBR, based on instantiated Abstract Argumentation and referred to as AA-CBR, for problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. In addition, we study properties of explanations in AA-CBR and define a new notion of lean explanations that utilize solely relevant cases. Both forms of explanations can be seen as dialogical processes between a proponent and an opponent, with the burden of proof falling on the proponent
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
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
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