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dc.date.accessioned 2012-10-29T12:49:50Z
dc.date.available 2012-10-29T12:49:50Z
dc.date.issued 2002-10
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/23111
dc.description.abstract Argumentative formalisms have been widely recognized as knowledge representation and reasoning tools able to deal with incomplete and potentially contradictory informa- tion. All such formalisms are computationally demanding. Hence, optimizing argumen- tative systems has been approached from di erent views. We have developed a new proposal to solve the aforementioned problem. The key to our approach consists in maintaining a module associated with the knowledge base of the system, with additional information that may help to speed up the inference process. As truth maintenance systems optimize general problem solvers, this module could play a similar role in argumentative systems. en
dc.format.extent 217-228 es
dc.language es es
dc.subject knowledge representation en
dc.subject ARTIFICIAL INTELLIGENCE es
dc.subject argumentation en
dc.subject rational agents en
dc.title Precompiled knowledge support for dynamic argumentation es
dc.type Objeto de conferencia es
sedici.creator.person Capobianco, Marcela es
sedici.creator.person Chesñevar, Carlos Iván es
sedici.creator.person Simari, Guillermo Ricardo es
sedici.description.note Eje: Lógica e Inteligencia artificial es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.date.exposure 2002-10
sedici.relation.event VIII Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)