1,006 research outputs found
Improving argumentation-based recommender systems through context-adaptable selection criteria
Recommender Systems based on argumentation represent an important proposal where the recommendation is supported by qualitative information. In these systems, the role of the comparison criterion used to decide between competing arguments is paramount and the possibility of using the most appropriate for a given domain becomes a central issue; therefore, an argumentative recommender system that offers an interchangeable argument comparison criterion provides a significant ability that can be exploited by the user. However, in most of current recommender systems, the argument comparison criterion is either fixed, or codified within the arguments. In this work we propose a formalization of context-adaptable selection criteria that enhances the argumentative reasoning mechanism. Thus, we do not propose of a new type of recommender system; instead we present a mechanism that expand the capabilities of existing argumentation-based recommender systems. More precisely, our proposal is to provide a way of specifying how to select and use the most appropriate argument comparison criterion effecting the selection on the user´s preferences, giving the possibility of programming, by the use of conditional expressions, which argument preference criterion has to be used in each particular situation.Fil: Teze, Juan Carlos Lionel. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación; Argentina. Universidad Nacional de Entre RÃos; ArgentinaFil: Gottifredi, Sebastián. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación; ArgentinaFil: GarcÃa, Alejandro Javier. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación; Argentin
Belief Revision in Structured Probabilistic Argumentation
In real-world applications, knowledge bases consisting of all the information
at hand for a specific domain, along with the current state of affairs, are
bound to contain contradictory data coming from different sources, as well as
data with varying degrees of uncertainty attached. Likewise, an important
aspect of the effort associated with maintaining knowledge bases is deciding
what information is no longer useful; pieces of information (such as
intelligence reports) may be outdated, may come from sources that have recently
been discovered to be of low quality, or abundant evidence may be available
that contradicts them. In this paper, we propose a probabilistic structured
argumentation framework that arises from the extension of Presumptive
Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue
that this formalism is capable of addressing the basic issues of handling
contradictory and uncertain data. Then, to address the last issue, we focus on
the study of non-prioritized belief revision operations over probabilistic
PreDeLP programs. We propose a set of rationality postulates -- based on
well-known ones developed for classical knowledge bases -- that characterize
how such operations should behave, and study a class of operators along with
theoretical relationships with the proposed postulates, including a
representation theorem stating the equivalence between this class and the class
of operators characterized by the postulates
Recommender system based on argumentation by analogy
Argumentation has contributed to the formalization of a reasoning model, similar to the human reasoning. In general, argumentation can be associated with the interaction of reasons in favour and against certain conclusions, so as to determine what conclusions are acceptable. A way of arguing in which the way in which the arguments are constructed, is Defeasible Logic Programming (DeLP); this is a formalism that combines logic programming and defeasible argumentation. This work focuses on the strengthening of the reasoning process, identifying partial connections or determinations between knowledge pieces. Through these relations, it is possible to increase the justi cations and foundations that support a particular recommendation, by an analogy process.XV Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras de Informática (RedUNCI
Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support
A framework and methodology---termed LogiKEy---for the design and engineering
of ethical reasoners, normative theories and deontic logics is presented. The
overall motivation is the development of suitable means for the control and
governance of intelligent autonomous systems. LogiKEy's unifying formal
framework is based on semantical embeddings of deontic logics, logic
combinations and ethico-legal domain theories in expressive classic
higher-order logic (HOL). This meta-logical approach enables the provision of
powerful tool support in LogiKEy: off-the-shelf theorem provers and model
finders for HOL are assisting the LogiKEy designer of ethical intelligent
agents to flexibly experiment with underlying logics and their combinations,
with ethico-legal domain theories, and with concrete examples---all at the same
time. Continuous improvements of these off-the-shelf provers, without further
ado, leverage the reasoning performance in LogiKEy. Case studies, in which the
LogiKEy framework and methodology has been applied and tested, give evidence
that HOL's undecidability often does not hinder efficient experimentation.Comment: 50 pages; 10 figure
Towards an argument-based music recommender system
The significance of recommender systems has steadily grown in recent years as they help users to access relevant items from the vast universe of possibilities available these days. However, most of the research in recommenders is based purely on quantitative aspects, i.e., measures of similarity between items or users. In this paper we introduce a novel hybrid approach to refine recommendations achieved by quantitative methods with a qualitative approach based on argumentation, where suggestions are given after considering several arguments in favor or against the recommendations. In order to accomplish this, we use Defeasible Logic Programming (DeLP) as the underlying formalism for obtaining recommendations. This approach has a number of advantages over other existing recommendation techniques.In particular, recommendations can be refined at any time by adding new polished rules, and explanations may be provided supporting each recommendation in a way that can be easily understood by the user, by means of the computed arguments.Fil: Briguez, Cristian Emanuel. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e IngenierÃa de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; ArgentinaFil: Budan, Maximiliano Celmo David. Universidad Nacional del Sur. Departamento de Ciencia e IngenierÃa de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; ArgentinaFil: Deagustini, Cristhian Ariel David. Universidad Nacional del Sur. Departamento de Ciencia e IngenierÃa de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e IngenierÃa de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; ArgentinaFil: Capobianco, Marcela. Universidad Nacional del Sur. Departamento de Ciencia e IngenierÃa de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; ArgentinaFil: Simari, Guillermo Ricardo. Universidad Nacional del Sur. Departamento de Ciencia e IngenierÃa de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; 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
Solving Power and Trust Conflicts through Argumentation in Agent-mediated Knowledge Distribution
Distributing pieces of knowledge in large, usually distributed organizations is a central problem in Knowledge and Organization management. Policies for distributing knowledge and information are mostly incomplete or in potential conflict with each other. As a consequence, decision processes for information distribution may be difficult to formalize on the basis of a rationally justified procedure. This article presents an argumentative approach to cope with this problem based on integrating the JITIK multiagent system with Defeasible Logic Programming (DeLP), a logic programming formalism for defeasible argumentation. We show how power relations, as well as delegation and trust, can be embedded within our framework in terms of DeLP, in such a way that a dialectical argumentation process works as a decision core. Conflicts among policies are solved on the basis of a dialectical analysis whose outcome determines to which specific users different pieces of knowledge are to be delivered.Fil: Chesñevar, Carlos Iván. Universitat de Lleida; España. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca; ArgentinaFil: Brena, Ramón. Centro de Sistemas Inteligentes, Tecnológico de Monterrey; MéxicoFil: Aguirre, José L.. Centro de Sistemas Inteligentes, Tecnológico de Monterrey; Méxic
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