9,360 research outputs found
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
Constraint Programming (CP) has proved an effective paradigm to model and
solve difficult combinatorial satisfaction and optimisation problems from
disparate domains. Many such problems arising from the commercial world are
permeated by data uncertainty. Existing CP approaches that accommodate
uncertainty are less suited to uncertainty arising due to incomplete and
erroneous data, because they do not build reliable models and solutions
guaranteed to address the user's genuine problem as she perceives it. Other
fields such as reliable computation offer combinations of models and associated
methods to handle these types of uncertain data, but lack an expressive
framework characterising the resolution methodology independently of the model.
We present a unifying framework that extends the CP formalism in both model
and solutions, to tackle ill-defined combinatorial problems with incomplete or
erroneous data. The certainty closure framework brings together modelling and
solving methodologies from different fields into the CP paradigm to provide
reliable and efficient approches for uncertain constraint problems. We
demonstrate the applicability of the framework on a case study in network
diagnosis. We define resolution forms that give generic templates, and their
associated operational semantics, to derive practical solution methods for
reliable solutions.Comment: Revised versio
Towards the realisation of an integratated decision support environment for organisational decision making
Traditional decision support systems are based on the paradigm of a single decision maker working at a stand‐alone computer or terminal who has a specific decision to make with a specific goal in mind. Organizational decision support systems aim to support decision makers at all levels of an organization (from executive, middle management managers to operators), who have a variety of decisions to make, with different priorities, often in a distributed and dynamic environment. Such systems need to be designed and developed with extra functionality to meet the challenges such as collaborative working. This paper proposes an Integrated Decision Support Environment (IDSE) for organizational decision making. The IDSE distinguishes itself from traditional decision support systems in that it can flexibly configure and re‐configure its functions to support various decision applications. IDSE is an open software platform which allows its users to define their own decision processes and choose their own exiting decision tools to be integrated into the platform. The IDSE is designed and developed based on distributed client/server networking, with a multi‐tier integration framework for consistent information exchange and sharing, seamless process co‐ordination and synchronisation, and quick access to packaged and legacy systems. The prototype of the IDSE demonstrates good performance in agile response to fast changing decision situations
Logic Programming as Constructivism
The features of logic programming that
seem unconventional from the viewpoint of classical logic
can be explained in terms of constructivistic logic. We
motivate and propose a constructivistic proof theory of
non-Horn logic programming. Then, we apply this formalization
for establishing results of practical interest.
First, we show that 'stratification can be motivated in a
simple and intuitive way. Relying on similar motivations,
we introduce the larger classes of 'loosely stratified' and
'constructively consistent' programs. Second, we give a
formal basis for introducing quantifiers into queries and
logic programs by defining 'constructively domain
independent* formulas. Third, we extend the Generalized
Magic Sets procedure to loosely stratified and constructively
consistent programs, by relying on a 'conditional
fixpoini procedure
Towards concept analysis in categories: limit inferior as algebra, limit superior as coalgebra
While computer programs and logical theories begin by declaring the concepts
of interest, be it as data types or as predicates, network computation does not
allow such global declarations, and requires *concept mining* and *concept
analysis* to extract shared semantics for different network nodes. Powerful
semantic analysis systems have been the drivers of nearly all paradigm shifts
on the web. In categorical terms, most of them can be described as
bicompletions of enriched matrices, generalizing the Dedekind-MacNeille-style
completions from posets to suitably enriched categories. Yet it has been well
known for more than 40 years that ordinary categories themselves in general do
not permit such completions. Armed with this new semantical view of
Dedekind-MacNeille completions, and of matrix bicompletions, we take another
look at this ancient mystery. It turns out that simple categorical versions of
the *limit superior* and *limit inferior* operations characterize a general
notion of Dedekind-MacNeille completion, that seems to be appropriate for
ordinary categories, and boils down to the more familiar enriched versions when
the limits inferior and superior coincide. This explains away the apparent gap
among the completions of ordinary categories, and broadens the path towards
categorical concept mining and analysis, opened in previous work.Comment: 22 pages, 5 figures and 9 diagram
Loop Formulas for Description Logic Programs
Description Logic Programs (dl-programs) proposed by Eiter et al. constitute
an elegant yet powerful formalism for the integration of answer set programming
with description logics, for the Semantic Web. In this paper, we generalize the
notions of completion and loop formulas of logic programs to description logic
programs and show that the answer sets of a dl-program can be precisely
captured by the models of its completion and loop formulas. Furthermore, we
propose a new, alternative semantics for dl-programs, called the {\em canonical
answer set semantics}, which is defined by the models of completion that
satisfy what are called canonical loop formulas. A desirable property of
canonical answer sets is that they are free of circular justifications. Some
properties of canonical answer sets are also explored.Comment: 29 pages, 1 figures (in pdf), a short version appeared in ICLP'1
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