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dc.contributor.author
Cohen, Andrea
dc.contributor.author
Gottifredi, Sebastián
dc.contributor.author
Vallati, Mauro
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García, Alejandro Javier
dc.contributor.author
Grigoris, Antoniou
dc.date.available
2020-11-02T20:10:38Z
dc.date.issued
2020-03-06
dc.identifier.citation
Cohen, Andrea; Gottifredi, Sebastián; Vallati, Mauro; García, Alejandro Javier; Grigoris, Antoniou; Maximising goals achievement through abstract argumentation frameworks: An optimal approach; Pergamon-Elsevier Science Ltd; Expert Systems with Applications; 141; 6-3-2020; 1-12; 112930
dc.identifier.issn
0957-4174
dc.identifier.uri
http://hdl.handle.net/11336/117433
dc.description.abstract
Argumentation is a prominent AI research area, focused on approaches and techniques for performing common-sense reasoning, that is of paramount importance in a wide range of real-world applications, such as decision support and recommender systems. In this work we introduce an approach for updating an abstract Argumentation Framework (AF) so that achievement with respect to a given set of goals is maximised. The set of goals identifies arguments for which a specific acceptability status (a labelling) will be pursued, distinguishing between “in” and “out” goals. Given an AF, a set of goals and a set of available actions allowing to add or remove arguments and attacks from the AF, our approach will select the strategy (set of actions) that should be applied in order to obtain a new AF where the goals achievement is maximised. Moreover, the selected strategy will be optimal with respect to the number of actions to be applied. In the context of argumentation-based expert and intelligent systems, our approach will provide tools allowing the user to interact with the argumentative reasoning process carried out by the system, learning how the strategy she undertakes will affect the recommendations she receives. For that, we propose an encoding of the AF, the available actions and goals as weighted Boolean formulas, and rely on MaxSAT techniques for selecting the optimal strategy. We provide an experimental analysis of our approach, and formally show that the results we obtain correspond to the optimal strategy.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Pergamon-Elsevier Science Ltd
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ABSTRACT ARGUMENTATION
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ARGUMENTATION DYNAMICS
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GOALS ACHIEVEMENT
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MAXSAT
dc.subject.classification
Ciencias de la Computación
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Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Maximising goals achievement through abstract argumentation frameworks: An optimal approach
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-02-26T19:34:20Z
dc.journal.volume
141
dc.journal.pagination
1-12; 112930
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Cambridge
dc.description.fil
Fil: Cohen, Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
dc.description.fil
Fil: Gottifredi, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
dc.description.fil
Fil: Vallati, Mauro. University of Huddersfield. School of Computing and Engineering; Reino Unido
dc.description.fil
Fil: García, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
dc.description.fil
Fil: Grigoris, Antoniou. University of Huddersfield. School of Computing and Engineering; Reino Unido
dc.journal.title
Expert Systems with Applications
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0957417419306487
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.eswa.2019.112930
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