779 research outputs found
A Framework for Combining Defeasible Argumentation with Labeled Deduction
In the last years, there has been an increasing demand of a variety of
logical systems, prompted mostly by applications of logic in AI and other
related areas. Labeled Deductive Systems (LDS) were developed as a flexible
methodology to formalize such a kind of complex logical systems. Defeasible
argumentation has proven to be a successful approach to formalizing commonsense
reasoning, encompassing many other alternative formalisms for defeasible
reasoning. Argument-based frameworks share some common notions (such as the
concept of argument, defeater, etc.) along with a number of particular features
which make it difficult to compare them with each other from a logical
viewpoint. This paper introduces LDSar, a LDS for defeasible argumentation in
which many important issues concerning defeasible argumentation are captured
within a unified logical framework. We also discuss some logical properties and
extensions that emerge from the proposed framework.Comment: 15 pages, presented at CMSRA Workshop 2003. Buenos Aires, 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intelligence reports) may be outdated, may come from sources that have recently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
A Parameterised Hierarchy of Argumentation Semantics for Extended Logic Programming and its Application to the Well-founded Semantics
Argumentation has proved a useful tool in defining formal semantics for
assumption-based reasoning by viewing a proof as a process in which proponents
and opponents attack each others arguments by undercuts (attack to an
argument's premise) and rebuts (attack to an argument's conclusion). In this
paper, we formulate a variety of notions of attack for extended logic programs
from combinations of undercuts and rebuts and define a general hierarchy of
argumentation semantics parameterised by the notions of attack chosen by
proponent and opponent. We prove the equivalence and subset relationships
between the semantics and examine some essential properties concerning
consistency and the coherence principle, which relates default negation and
explicit negation. Most significantly, we place existing semantics put forward
in the literature in our hierarchy and identify a particular argumentation
semantics for which we prove equivalence to the paraconsistent well-founded
semantics with explicit negation, WFSX. Finally, we present a general proof
theory, based on dialogue trees, and show that it is sound and complete with
respect to the argumentation semantics.Comment: To appear in Theory and Practice of Logic Programmin
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Value-based argumentation frameworks as neural-symbolic learning systems
While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argumentation algorithm, responsible for translating argumentation networks into standard neural networks. We then show a correspondence between the two networks. The algorithm works not only for acyclic argumentation networks, but also for circular networks, and it enables the accrual of arguments through learning as well as the parallel computation of arguments
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
Embedding defeasible argumentation in the semantic web: an ontology-based approach
The SemanticWeb is a project intended to create a universal medium for information exchange by giving semantics to the content of documents on the Web by means of ontology definitions.
Ontologies intended for knowledge representation in intelligent agents rely on common-sense reasoning formalizations. Defeasible argumentation has emerged as a successful approach to model common-sense reasoning. Recent research has linked argumentation with belief revision in order to model the dynamics of knowledge. This paper outlines an approach which combines ontologies, argumentation and belief revision by defining an ontology algebra. We suggest how different aspects of ontology integration can be defined in terms of defeasible argumentation and belief revision.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI
Some Classical Problems of Inheritance Networks in the Light of Defeasible Ontology Reasoning
Reasoning with possibly inconsistent ontologies is an important reasearch topic for the implementation of the Semantic Web as they pose a problem for performing instance checking. We contend that Defeasible Logic Programming (DeLP) is a reliable tool for doing ontology reasoning when Description Logic ontologies can be interpreted as DeLP programs. In this work we present some classical problems of the eld of inheritance networks and show how they are modeled as inconsistent ontologies and thus how the problem of instance checking is solved; we also show how issues in reasoning with argumentation frameworks based on Dung's grounded semantics are also solved when applied to ontology reasoning, and we revise the main algorithm for instance checking when using DeLP with inconsistent ontologies.Eje: XV Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras de Informática (RedUNCI
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