9,241 research outputs found
Answer Sets for Logic Programs with Arbitrary Abstract Constraint Atoms
In this paper, we present two alternative approaches to defining answer sets
for logic programs with arbitrary types of abstract constraint atoms (c-atoms).
These approaches generalize the fixpoint-based and the level mapping based
answer set semantics of normal logic programs to the case of logic programs
with arbitrary types of c-atoms. The results are four different answer set
definitions which are equivalent when applied to normal logic programs. The
standard fixpoint-based semantics of logic programs is generalized in two
directions, called answer set by reduct and answer set by complement. These
definitions, which differ from each other in the treatment of
negation-as-failure (naf) atoms, make use of an immediate consequence operator
to perform answer set checking, whose definition relies on the notion of
conditional satisfaction of c-atoms w.r.t. a pair of interpretations. The other
two definitions, called strongly and weakly well-supported models, are
generalizations of the notion of well-supported models of normal logic programs
to the case of programs with c-atoms. As for the case of fixpoint-based
semantics, the difference between these two definitions is rooted in the
treatment of naf atoms. We prove that answer sets by reduct (resp. by
complement) are equivalent to weakly (resp. strongly) well-supported models of
a program, thus generalizing the theorem on the correspondence between stable
models and well-supported models of a normal logic program to the class of
programs with c-atoms. We show that the newly defined semantics coincide with
previously introduced semantics for logic programs with monotone c-atoms, and
they extend the original answer set semantics of normal logic programs. We also
study some properties of answer sets of programs with c-atoms, and relate our
definitions to several semantics for logic programs with aggregates presented
in the literature
A Comparison of Big Data Frameworks on a Layered Dataflow Model
In the world of Big Data analytics, there is a series of tools aiming at
simplifying programming applications to be executed on clusters. Although each
tool claims to provide better programming, data and execution models, for which
only informal (and often confusing) semantics is generally provided, all share
a common underlying model, namely, the Dataflow model. The Dataflow model we
propose shows how various tools share the same expressiveness at different
levels of abstraction. The contribution of this work is twofold: first, we show
that the proposed model is (at least) as general as existing batch and
streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to
understand high-level data-processing applications written in such frameworks.
Second, we provide a layered model that can represent tools and applications
following the Dataflow paradigm and we show how the analyzed tools fit in each
level.Comment: 19 pages, 6 figures, 2 tables, In Proc. of the 9th Intl Symposium on
High-Level Parallel Programming and Applications (HLPP), July 4-5 2016,
Muenster, German
A Survey of Languages for Specifying Dynamics: A Knowledge Engineering Perspective
A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machine
Intensional Cyberforensics
This work focuses on the application of intensional logic to cyberforensic
analysis and its benefits and difficulties are compared with the
finite-state-automata approach. This work extends the use of the intensional
programming paradigm to the modeling and implementation of a cyberforensics
investigation process with backtracing of event reconstruction, in which
evidence is modeled by multidimensional hierarchical contexts, and proofs or
disproofs of claims are undertaken in an eductive manner of evaluation. This
approach is a practical, context-aware improvement over the finite state
automata (FSA) approach we have seen in previous work. As a base implementation
language model, we use in this approach a new dialect of the Lucid programming
language, called Forensic Lucid, and we focus on defining hierarchical contexts
based on intensional logic for the distributed evaluation of cyberforensic
expressions. We also augment the work with credibility factors surrounding
digital evidence and witness accounts, which have not been previously modeled.
The Forensic Lucid programming language, used for this intensional
cyberforensic analysis, formally presented through its syntax and operational
semantics. In large part, the language is based on its predecessor and
codecessor Lucid dialects, such as GIPL, Indexical Lucid, Lucx, Objective
Lucid, and JOOIP bound by the underlying intensional programming paradigm.Comment: 412 pages, 94 figures, 18 tables, 19 algorithms and listings; PhD
thesis; v2 corrects some typos and refs; also available on Spectrum at
http://spectrum.library.concordia.ca/977460
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