75,036 research outputs found
Coherent Integration of Databases by Abductive Logic Programming
We introduce an abductive method for a coherent integration of independent
data-sources. The idea is to compute a list of data-facts that should be
inserted to the amalgamated database or retracted from it in order to restore
its consistency. This method is implemented by an abductive solver, called
Asystem, that applies SLDNFA-resolution on a meta-theory that relates
different, possibly contradicting, input databases. We also give a pure
model-theoretic analysis of the possible ways to `recover' consistent data from
an inconsistent database in terms of those models of the database that exhibit
as minimal inconsistent information as reasonably possible. This allows us to
characterize the `recovered databases' in terms of the `preferred' (i.e., most
consistent) models of the theory. The outcome is an abductive-based application
that is sound and complete with respect to a corresponding model-based,
preferential semantics, and -- to the best of our knowledge -- is more
expressive (thus more general) than any other implementation of coherent
integration of databases
Answer Set Planning Under Action Costs
Recently, planning based on answer set programming has been proposed as an
approach towards realizing declarative planning systems. In this paper, we
present the language Kc, which extends the declarative planning language K by
action costs. Kc provides the notion of admissible and optimal plans, which are
plans whose overall action costs are within a given limit resp. minimum over
all plans (i.e., cheapest plans). As we demonstrate, this novel language allows
for expressing some nontrivial planning tasks in a declarative way.
Furthermore, it can be utilized for representing planning problems under other
optimality criteria, such as computing ``shortest'' plans (with the least
number of steps), and refinement combinations of cheapest and fastest plans. We
study complexity aspects of the language Kc and provide a transformation to
logic programs, such that planning problems are solved via answer set
programming. Furthermore, we report experimental results on selected problems.
Our experience is encouraging that answer set planning may be a valuable
approach to expressive planning systems in which intricate planning problems
can be naturally specified and solved
Preferential and Preferential-discriminative Consequence relations
The present paper investigates consequence relations that are both
non-monotonic and paraconsistent. More precisely, we put the focus on
preferential consequence relations, i.e. those relations that can be defined by
a binary preference relation on states labelled by valuations. We worked with a
general notion of valuation that covers e.g. the classical valuations as well
as certain kinds of many-valued valuations. In the many-valued cases,
preferential consequence relations are paraconsistant (in addition to be
non-monotonic), i.e. they are capable of drawing reasonable conclusions which
contain contradictions. The first purpose of this paper is to provide in our
general framework syntactic characterizations of several families of
preferential relations. The second and main purpose is to provide, again in our
general framework, characterizations of several families of preferential
discriminative consequence relations. They are defined exactly as the plain
version, but any conclusion such that its negation is also a conclusion is
rejected (these relations bring something new essentially in the many-valued
cases).Comment: team Logic and Complexity, written in 2004-200
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An intelligent system for risk classification of stock investment projects
The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange
Experimentally exploring compressed sensing quantum tomography
In the light of the progress in quantum technologies, the task of verifying
the correct functioning of processes and obtaining accurate tomographic
information about quantum states becomes increasingly important. Compressed
sensing, a machinery derived from the theory of signal processing, has emerged
as a feasible tool to perform robust and significantly more resource-economical
quantum state tomography for intermediate-sized quantum systems. In this work,
we provide a comprehensive analysis of compressed sensing tomography in the
regime in which tomographically complete data is available with reliable
statistics from experimental observations of a multi-mode photonic
architecture. Due to the fact that the data is known with high statistical
significance, we are in a position to systematically explore the quality of
reconstruction depending on the number of employed measurement settings,
randomly selected from the complete set of data, and on different model
assumptions. We present and test a complete prescription to perform efficient
compressed sensing and are able to reliably use notions of model selection and
cross-validation to account for experimental imperfections and finite counting
statistics. Thus, we establish compressed sensing as an effective tool for
quantum state tomography, specifically suited for photonic systems.Comment: 12 pages, 5 figure
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