256 research outputs found

    Consequence operators for defeasible argumentation: characterization and logical properties

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    Artificial Intelligence (AI) has long dealt with the issue of finding a suitable formalization for commonsense reasoning. Defeasible argumentation has proven to be a successful approach in many respects, proving to be a confluence point for many alternative logical frameworks. Different formalisms have been developed, most of them sharing the common notions of argument and warrant. In defeasible argumentation, an argument is a tentative (defeasible) proof for reaching a conclusion. An argument is warranted when it ultimately prevails over other con°icting arguments. In this context, defeasible consequence relationships for modeling argument and warrant as well as their logical properties have gained particular attention. This paper discusses two consequence operators for the LDSar framework for defeasible argumentation. The operators are intended for modeling argument construction and dialectical analysis (warrant), respectively. Their associated logical properties are studied and contrasted with SLD-based Horn logic. We contend that this analysis provides useful comparison criteria that can be extended and applied to other argumentation frameworks.Eje: Informática teóricaRed de Universidades con Carreras en Informática (RedUNCI

    Modeling argumentation with labeled deduction: formalization and theoretical considerations

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    In the last years there has been an increasing demand of a variety of logical systems, prompted mostly by applications of logic in AI, logic programming and other related areas. Labeled Deductive Systems (LDS) were developed as a °exible methodology to formalize such a kind of complex logical systems. During the last decade defeasible argumentation has proven to be a con°uence point for many approaches to formalizing commonsense reasoning. Di®erent formalisms have been developed, many of them sharing common features. This paper summarizes the most relevant features of LDSar, a logical framework for defeasible argumentation based on LDS. We present a syntactic characterization of the framework, and discuss some emerging properties. We also show how di®erent existing argumentation frameworks are subsumed in LDSarEje: Informática teóricaRed de Universidades con Carreras en Informática (RedUNCI

    A logic programming framework for possibilistic argumentation: formalization and logical properties

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    In the last decade defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning. The logic programming paradigm has shown to be particularly useful for developing different argument-based frameworks on the basis of different variants of logic programming which incorporate defeasible rules. Most of such frameworks, however, are unable to deal with explicit uncertainty, nor with vague knowledge, as defeasibility is directly encoded in the object language. This paper presents Possibilistic Logic Programming (P-DeLP), a new logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty. Such features are formalized on the basis of PGL, a possibilistic logic based on G¨odel fuzzy logic. One of the applications of P-DeLP is providing an intelligent agent with non-monotonic, argumentative inference capabilities. In this paper we also provide a better understanding of such capabilities by defining two non-monotonic operators which model the expansion of a given program P by adding new weighed facts associated with argument conclusions and warranted literals, respectively. Different logical properties for the proposed operators are studie

    Towards possibilistic fuzzy answer set programming

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    Fuzzy answer set programming (FASP) is a generalization of answer set programming to continuous domains. As it can not readily take uncertainty into account, however, FASP is not suitable as a basis for approximate reasoning and cannot easily be used to derive conclusions from imprecise information. To cope with this, we propose an extension of FASP based on possibility theory. The resulting framework allows us to reason about uncertain information in continuous domains, and thus also about information that is imprecise or vague. We propose a syntactic procedure, based on an immediate consequence operator, and provide a characterization in terms of minimal models, which allows us to straightforwardly implement our framework using existing FASP solvers

    Consequence operators for defeasible argumentation: characterization and logical properties

    Get PDF
    Artificial Intelligence (AI) has long dealt with the issue of finding a suitable formalization for commonsense reasoning. Defeasible argumentation has proven to be a successful approach in many respects, proving to be a confluence point for many alternative logical frameworks. Different formalisms have been developed, most of them sharing the common notions of argument and warrant. In defeasible argumentation, an argument is a tentative (defeasible) proof for reaching a conclusion. An argument is warranted when it ultimately prevails over other con°icting arguments. In this context, defeasible consequence relationships for modeling argument and warrant as well as their logical properties have gained particular attention. This paper discusses two consequence operators for the LDSar framework for defeasible argumentation. The operators are intended for modeling argument construction and dialectical analysis (warrant), respectively. Their associated logical properties are studied and contrasted with SLD-based Horn logic. We contend that this analysis provides useful comparison criteria that can be extended and applied to other argumentation frameworks.Eje: Informática teóricaRed de Universidades con Carreras en Informática (RedUNCI

    An argumentation framework with uncertainty management designed for dynamic environments

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    Nowadays, data intensive applications are in constant demand and there is need of computing environments with better intelligent capabilities than those present in today's Database Management Systems (DBMS). To build such systems we need formalisms that can perform complicate inferences, obtain the appropriate conclusions, and explain the results. Research in argumentation could provide results in this direction, providing means to build interactive systems able to reason with large databases and/or di erent data sources. In this paper we propose an argumentation system able to deal with explicit uncertainty, a vital capability in modern applications. We have also provided the system with the ability to seamlessly incorporate uncertain and/or contradictory information into its knowledge base, using a modular upgrading and revision procedurePresentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI
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