1,761 research outputs found
A structured argumentation framework for detaching conditional obligations
We present a general formal argumentation system for dealing with the
detachment of conditional obligations. Given a set of facts, constraints, and
conditional obligations, we answer the question whether an unconditional
obligation is detachable by considering reasons for and against its detachment.
For the evaluation of arguments in favor of detaching obligations we use a
Dung-style argumentation-theoretical semantics. We illustrate the modularity of
the general framework by considering some extensions, and we compare the
framework to some related approaches from the literature.Comment: This is our submission to DEON 2016, including the technical appendi
Optimizing the computation of overriding
We introduce optimization techniques for reasoning in DLN---a recently
introduced family of nonmonotonic description logics whose characterizing
features appear well-suited to model the applicative examples naturally arising
in biomedical domains and semantic web access control policies. Such
optimizations are validated experimentally on large KBs with more than 30K
axioms. Speedups exceed 1 order of magnitude. For the first time, response
times compatible with real-time reasoning are obtained with nonmonotonic KBs of
this size
The development of an expert system shell with a mixed knowledge representation, explicit control of reasoning and a truth maintenance system
Bibliography: pages 227-236.This thesis concentrates on several important issues in expert system research, namely - representation of knowledge - control of reasoning - implementation of non-monotonic logics via truth maintenance systems. There are three parts to this thesis. PART1 covers the background research in the above mentioned topics. PART2 discusses the WISE system and the way in which research from PART1 was applied to the development of the WISE shell. PART3 considers the features of other expert system shells
Deontic Logic and Natural Language
There has been a recent surge of work on deontic modality within philosophy of language. This work has put the deontic logic tradition in contact with natural language semantics, resulting in significant increase in sophistication on both ends. This chapter surveys the main motivations, achievements, and prospects of this work
OWL-POLAR : A Framework for Semantic Policy Representation and Reasoning
Peer reviewedPreprin
ESPOON: Enforcing Security Policies In Outsourced Environments
Data outsourcing is a growing business model offering services to individuals
and enterprises for processing and storing a huge amount of data. It is not
only economical but also promises higher availability, scalability, and more
effective quality of service than in-house solutions. Despite all its benefits,
data outsourcing raises serious security concerns for preserving data
confidentiality. There are solutions for preserving confidentiality of data
while supporting search on the data stored in outsourced environments. However,
such solutions do not support access policies to regulate access to a
particular subset of the stored data.
For complex user management, large enterprises employ Role-Based Access
Controls (RBAC) models for making access decisions based on the role in which a
user is active in. However, RBAC models cannot be deployed in outsourced
environments as they rely on trusted infrastructure in order to regulate access
to the data. The deployment of RBAC models may reveal private information about
sensitive data they aim to protect. In this paper, we aim at filling this gap
by proposing \textbf{} for enforcing RBAC policies in
outsourced environments. enforces RBAC policies in an
encrypted manner where a curious service provider may learn a very limited
information about RBAC policies. We have implemented
and provided its performance evaluation showing a limited overhead, thus
confirming viability of our approach.Comment: The final version of this paper has been accepted for publication in
Elsevier Computers & Security 2013. arXiv admin note: text overlap with
arXiv:1306.482
An Empirical Evaluation of the Inferential Capacity of Defeasible Argumentation, Non-monotonic Fuzzy Reasoning and Expert Systems
Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the examination of their inferential capacity. Thus, this paper focuses on a comparison of three knowledge-driven approaches employed for non-monotonic reasoning, namely expert systems, fuzzy reasoning and defeasible argumentation. A knowledge-representation and reasoning problem has been selected: modelling and assessing mental workload. This is an ill-defined construct, and its formalisation can be seen as a reasoning activity under uncertainty. An experimental work was performed by exploiting three deductive knowledge bases produced with the aid of experts in the field. These were coded into models by employing the selected techniques and were subsequently elicited with data gathered from humans. The inferences produced by these models were in turn analysed according to common metrics of evaluation in the field of mental workload, in specific validity and sensitivity. Findings suggest that the variance of the inferences of expert systems and fuzzy reasoning models was higher, highlighting poor stability. Contrarily, that of argument-based models was lower, showing a superior stability of its inferences across knowledge bases and under different system configurations. The originality of this research lies in the quantification of the impact of defeasible argumentation. It contributes to the field of logic and non-monotonic reasoning by situating defeasible argumentation among similar approaches of non-monotonic reasoning under uncertainty through a novel empirical comparison
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