109 research outputs found
A logic programming framework for possibilistic argumentation: formalization and logical properties
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
An argumentation framework with uncertainty management designed for dynamic environments
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
A Constrained, Possibilistic Logical Approach for Software System Survivability Evaluation
In this paper, we present a logical framework to facilitate users in assessing a software system in terms of the required survivability features. Survivability evaluation is essential in linking foreign software components to an existing system or obtaining software systems from external sources. It is important to make sure that any foreign components/systems will not compromise the current system’s survivability properties. Given the increasing large scope and complexity of modern software systems, there is a need for an evaluation framework to accommodate uncertain, vague, or even ill-known knowledge for a robust evaluation based on multi-dimensional criteria. Our framework incorporates user-defined constrains on survivability requirements. Necessity-based possibilistic uncertainty and user survivability requirement constraints are effectively linked to logic reasoning. A proof-of-concept system has been developed to validate the proposed approach. To our best knowledge, our work is the first attempt to incorporate vague, imprecise information into software system survivability evaluation
Weighted logics for artificial intelligence : an introductory discussion
International audienceBefore presenting the contents of the special issue, we propose a structured introductory overview of a landscape of the weighted logics (in a general sense) that can be found in the Artificial Intelligence literature, highlighting their fundamental differences and their application areas
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
A Compact Argumentation System for Agent System Specification
We present a non-monotonic logic tailored for specifying compact autonomous agent systems. The language is a consistent instantiation of a logic based argumentation system extended with Brooks' subsumption concept and varying degree of belief. Particularly, we present a practical implementation of the language by developing a meta-encoding method that translates logical specifications into compact general logic programs. The language allows n-ary predicate literals with the usual first-order term definitions. We show that the space complexity of the resulting general logic program is linear to the size of the original theory
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
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