475 research outputs found
On the Implementation of a Multiple Output Algorithm for Defeasible Argumentation
In a previous work we defined a recursive warrant semantics for Defeasible Logic Programming based on a general notion of collective conflict among arguments. The main feature of this recursive semantics is that an output of a program is a pair consisting of a set of warranted and a set of blocked formulas. A program may have multiple outputs in case of circular definitions of conflicts among arguments. In this paper we design an algorithm for computing each output and we provide an experimental evaluation of the algorithm based on two SAT encodings defined for the two main combinatorial subproblems that arise when computing warranted and blocked conclusions for each output.The authors acknowledge the Spanish projects ARINF (TIN2009-14704-C03-01), TASSAT (TIN2010-20967-C04-03) and EdeTRI (TIN2012-39348-C02-01).Peer Reviewe
RP-DeLP: a weighted defeasible argumentation framework based on a recursive semantics
In this paper we first define a recursive semantics for warranted formulas in a general defeasible argumentation framework by formalizing a notion of collective (non-binary) conflict among arguments. The recursive semantics for warranted formulas is based on the fact that if the argument is rejected, then all arguments built on it should also be rejected. The main characteristic of our recursive semantics is that an output (extension) of a knowledge base is a pair of sets of warranted and blocked formulas. Arguments for both warranted and blocked formulas are recursively based on warranted formulas but, while warranted formulas do not generate any collective conflict, blocked conclusions do. Formulas that are neither warranted nor blocked correspond to rejected formulas. Second we extend the general defeasible argumentation framework by attaching levels of preference to defeasible knowledge items and by providing a level-wise definition of warranted and blocked formulas. Third we formalize the warrant recursive semantics for the particular framework of Possibilistic Defeasible Logic Programming, we call this particular framework Recursive Possibilistic Defeasible Logic Programming (\mbox{RP-DeLP} for short), and we show its relevance in the scope of Political debates. An RP-DeLP program may have multiple outputs in case of circular definitions of conflicts among arguments. So, we tackle the problem of which output one should consider for an RP-DeLP program with multiple outputs. To this end we define the maximal ideal output of an RP-DeLP program as the set of conclusions which are ultimately warranted and we present an algorithm for computing them in polynomial space and with an upper bound on complexity equal to P^{NP}. Finally, we propose an efficient and scalable implementation of this algorithm that is based on implementing the two main queries of the system, looking for valid arguments and collective conflicts between arguments, using SAT encodings. We perform an experimental evaluation of our SAT based approach when solving test sets of instances with single and multiple preference levels for defeasible knowledge.The authors are very thankful to the anonymous reviewers for their helpful and constructive
comments. This research was partially supported by the Spanish projects ARINF (TIN2009-
14704-C03-01), TASSAT (TIN2010-20967-C04-03), EdeTRI (TIN2012-39348-C02-01) and AT
(CONSOLIDER- INGENIO 2010, CSD2007-00022)
Formalisation and logical properties of the maximal ideal recursive semantics for weighted defeasible logic programming
Possibilistic defeasible logic programming (P-DeLP) is a logic programming framework which combines features from argumentation theory and logic programming, in which defeasible rules are attached with weights expressing their relative belief or preference strength. In P-DeLP,a conclusion succeeds if there exists an argument that entails the conclusion and this argument is found to be undefeated by a warrant procedure that systematically explores the universe of arguments in order to present an exhaustive synthesis of the relevant chains of pros and cons for the given conclusion. Recently, we have proposed a new warrant recursive semantics for P-DeLP, called Recursive P-DeLP (RP-DeLP for short), based on the claim that the acceptance of an argument should imply also the acceptance of all its sub-arguments which reflect the different premises on which the argument is based. This paper explores the relationship between the exhaustive dialectical analysis-based semantics of P-DeLP and the recursive-based semantics of RP-DeLP, and analyses a non-monotonic inference operator for RP-DeLP which models the expansion of a given program by adding new weighted facts associated with warranted conclusions. Given the recursive-based semantics of RP-DeLP, we have also implemented an argumentation framework for RP-DeLP that is able to compute not only the output of warranted and blocked conclusions, but also explain the reasons behind the status of each conclusion. We have developed this framework as a stand-alone application with a simple text-based input/output interface to be able to use it as part of other artificial intelligence systemsThis research was partially supported by the Spanish projects EdeTRI (TIN2012-39348-C02-01)
and AT (CONSOLIDER- INGENIO 2010, CSD2007-00022)
Argumentation-based query answering under uncertainty with application to cybersecurity
Decision support tools are key components of intelligent sociotechnical systems, and their successful implementation faces a variety of challenges, including the multiplicity of information sources, heterogeneous format, and constant changes. Handling such challenges requires the ability to analyze and process inconsistent and incomplete information with varying degrees of associated uncertainty. Moreover, some domains require the system’s outputs to be explainable and interpretable; an example of this is cyberthreat analysis (CTA) in cybersecurity domains. In this paper, we first present the P-DAQAP system, an extension of a recently developed query-answering platform based on defeasible logic programming (DeLP) that incorporates a probabilistic model and focuses on delivering these capabilities. After discussing the details of its design and implementation, and describing how it can be applied in a CTA use case, we report on the results of an empirical evaluation designed to explore the effectiveness and efficiency of a possible world sampling-based approximate query answering approach that addresses the intractability of exact computations.Fil: Leiva, Mario Alejandro. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Ciencias e IngenierÃa de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación. Instituto de Ciencias e IngenierÃa de la Computación; ArgentinaFil: GarcÃa, Alejandro Javier. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Ciencias e IngenierÃa de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación. Instituto de Ciencias e IngenierÃa de la Computación; ArgentinaFil: Shakarian, Paulo. Arizona State University; Estados UnidosFil: Simari, Gerardo. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Ciencias e IngenierÃa de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación. Instituto de Ciencias e IngenierÃa de la Computación; Argentin
Combining argumentation and clustering techniques in pattern classification problems
Clustering techniques can be used as a basis for classification systems in which clusters can be classified into two categories: positive and negative. Given a new instance enew, the classification algorithm is applied to determine to which cluster ci it belongs and the label of the cluster is checked. In such a setting clusters can overlap, and a new instance (or example) can be assigned to more than one cluster. In many cases, determining to which cluster this new instance actually belongs requires a qualitative analysis rather than a numerical one.
In this paper we present a novel approach to solve this problem by combining defeasible argumentation and a clustering algorithm based on the Fuzzy Adaptive Resonance Theory neural network model. The proposed approach takes as input a clustering algorithm and a background theory. Given a previously unseen instance enew, it will be classified using the clustering algorithm. If a conflicting situation arises, argumentation will be used in order to consider the user’s preference criteria for classifying examples.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI
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A neural cognitive model of argumentation with application to legal inference and decision making
Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data)
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