968 research outputs found
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intelligence reports) may be outdated, may come from sources that have recently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
An Argumentation-Based Reasoner to Assist Digital Investigation and Attribution of Cyber-Attacks
We expect an increase in the frequency and severity of cyber-attacks that
comes along with the need for efficient security countermeasures. The process
of attributing a cyber-attack helps to construct efficient and targeted
mitigating and preventive security measures. In this work, we propose an
argumentation-based reasoner (ABR) as a proof-of-concept tool that can help a
forensics analyst during the analysis of forensic evidence and the attribution
process. Given the evidence collected from a cyber-attack, our reasoner can
assist the analyst during the investigation process, by helping him/her to
analyze the evidence and identify who performed the attack. Furthermore, it
suggests to the analyst where to focus further analyses by giving hints of the
missing evidence or new investigation paths to follow. ABR is the first
automatic reasoner that can combine both technical and social evidence in the
analysis of a cyber-attack, and that can also cope with incomplete and
conflicting information. To illustrate how ABR can assist in the analysis and
attribution of cyber-attacks we have used examples of cyber-attacks and their
analyses as reported in publicly available reports and online literature. We do
not mean to either agree or disagree with the analyses presented therein or
reach attribution conclusions
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 Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: García, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Shakarian, Paulo. Arizona State University; Estados UnidosFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin
Argumentación probabilística y revisión de creencias con aplicaciones a ciberseguridad
Esta línea de investigación se centra en los aspectos algorítmicos y de representación de conocimiento y razonamiento asociados con los procesos de razonamiento dialéctico y dinámica del conocimiento bajo incertidumbre probabilística. La investigación es conducida por la aplicación de éstos en entornos relacionados con ciberseguridad y ciberguerra; dada la aplicabilidad de los resultados en datos provenientes del mundo real, la tratabilidad computacional es un tema central del proyecto.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI
Argumentación probabilística y revisión de creencias con aplicaciones a ciberseguridad
Esta línea de investigación se centra en los aspectos algorítmicos y de representación de conocimiento y razonamiento asociados con los procesos de razonamiento dialéctico y dinámica del conocimiento bajo incertidumbre probabilística. La investigación es conducida por la aplicación de éstos en entornos relacionados con ciberseguridad y ciberguerra; dada la aplicabilidad de los resultados en datos provenientes del mundo real, la tratabilidad computacional es un tema central del proyecto.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI
Argumentación probabilística y revisión de creencias con aplicaciones a ciberseguridad
Esta línea de investigación se centra en los aspectos algorítmicos y de representación de conocimiento y razonamiento asociados con los procesos de razonamiento dialéctico y dinámica del conocimiento bajo incertidumbre probabilística. La investigación es conducida por la aplicación de éstos en entornos relacionados con ciberseguridad y ciberguerra; dada la aplicabilidad de los resultados en datos provenientes del mundo real, la tratabilidad computacional es un tema central del proyecto.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI
Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives
Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future
Argument Strength in Probabilistic Argumentation Using Confirmation Theory
It is common for people to remark that a particular argument is a strong (or weak) argument. Having a handle on the relative strengths of arguments can help in deciding on which arguments to consider, and on which to present to others in a discussion. In computational models of argument, there is a need for a deeper understanding of argument strength. Our approach in this paper is to draw on confirmation theory for quantifying argument strength, and harness this in a framework based on probabilistic argumentation. We show how we can calculate strength based on the structure of the argument involving defeasible rules. The insights appear transferable to a variety of other structured argumentation systems
A PRISMA-driven systematic mapping study on system assurance weakeners
Context: An assurance case is a structured hierarchy of claims aiming at
demonstrating that a given mission-critical system supports specific
requirements (e.g., safety, security, privacy). The presence of assurance
weakeners (i.e., assurance deficits, logical fallacies) in assurance cases
reflects insufficient evidence, knowledge, or gaps in reasoning. These
weakeners can undermine confidence in assurance arguments, potentially
hindering the verification of mission-critical system capabilities.
Objectives: As a stepping stone for future research on assurance weakeners,
we aim to initiate the first comprehensive systematic mapping study on this
subject. Methods: We followed the well-established PRISMA 2020 and SEGRESS
guidelines to conduct our systematic mapping study. We searched for primary
studies in five digital libraries and focused on the 2012-2023 publication year
range. Our selection criteria focused on studies addressing assurance weakeners
at the modeling level, resulting in the inclusion of 39 primary studies in our
systematic review.
Results: Our systematic mapping study reports a taxonomy (map) that provides
a uniform categorization of assurance weakeners and approaches proposed to
manage them at the modeling level.
Conclusion: Our study findings suggest that the SACM (Structured Assurance
Case Metamodel) -- a standard specified by the OMG (Object Management Group) --
may be the best specification to capture structured arguments and reason about
their potential assurance weakeners
Argument strength in probabilistic argumentation based on defeasible rules
It is common for people to remark that a particular argument is a strong (or weak) argument. Having a handle on the relative strengths of arguments can help in deciding on which arguments to consider, which arguments to regard as acceptable, and on which arguments to present to others in a discussion. In computational models of argument, there is a need for a deeper understanding of argument strength. It is a multidimensional problem, and in this paper, we focus on one aspect of argument strength for deductive argumentation based on a defeasible logic. We assume a probability distribution over models of the language and consider how there are various ways to calculate argument strength based on the probabilistic necessity and sufficiency of the premises for the claim, the probabilistic sufficiency of competing premises the claim, and the probabilistic necessity of the premises for competing claims. We provide axioms for characterizing probability-based measures of argument strength, and we investigate four specific probability-based measures
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