169 research outputs found
Rule-based Programming for Building Expert Systems: a Comparison in the Microbiological Data Validation and Surveillance Domain
Abstract In this work, we compare three rule-based programming tools used for building an expert system for microbiological laboratory data validation and bacteria infections monitoring. The first prototype of the system was implemented in KAPPA-PC. We report on the implementation and performance by comparing KAPPA-PC with two other more recent tools, namely JESS and ILOG JRULES. In order to test each tool we realized three simple test applications capable to perform some tasks that are peculiar of our expert system
Acinetobacter baumannii in intensive care unit: A novel system to study clonal relationship among the isolates
<p>Abstract</p> <p>Background</p> <p>The nosocomial infections surveillance system must be strongly effective especially in highly critic areas, such as Intensive Care Units (ICU). These areas are frequently an epidemiological epicentre for transmission of multi-resistant pathogens, like <it>Acinetobacter baumannii</it>. As an epidemic outbreak occurs it is very important to confirm or exclude the genetic relationship among the isolates in a short time. There are several molecular typing systems used with this aim. The Repetitive sequence-based PCR (REP-PCR) has been recognized as an effective method and it was recently adapted to an automated format known as the DiversiLab system.</p> <p>Methods</p> <p>In the present study we have evaluated the combination of a newly introduced software package for the control of hospital infection (VIGI@ct) with the DiversiLab system. In order to evaluate the reliability of the DiversiLab its results were also compared with those obtained using f-AFLP.</p> <p>Results</p> <p>The combination of VIGI@ct and DiversiLab enabled an earlier identification of an <it>A. baumannii </it>epidemic cluster, through the confirmation of the genetic relationship among the isolates. This cluster regards 56 multi-drug-resistant <it>A. baumannii </it>isolates from several specimens collected from 13 different patients admitted to the ICU in a ten month period. The <it>A. baumannii </it>isolates were clonally related being their similarity included between 97 and 100%. The results of the DiversiLab were confirmed by f-AFLP analysis.</p> <p>Conclusion</p> <p>The early identification of the outbreak has led to the prompt application of operative procedures and precautions to avoid the spread of pathogen. To date, 6 months after the last <it>A. baumannii </it>isolate, no other related case has been identified.</p
The frequency of tetracycline resistance genes co-detected with respiratory pathogens: a database mining study uncovering descriptive trends throughout the United States
Extensions of Logic Programming - Third International Workshop, ELP '92, Bologna, Italy, February 26-28, 1992
The volume collectes papers on extensions of Logic Programmin
An Interactive Constraint-Based System for Selective Attention in Visual Search
. We face in this paper the problem of model-based object recognition in a scene. Computer vision techniques usually separate the extraction of visual information from the scene from the reasoning on the symbolic data. We propose to interactively intertwine the two parts: the reasoning task on visual information is based on constraint satisfaction techniques. Objects are modeled by means of constraints and constraint propagation recognizes an object in the scene. We extend the classical Constraint Satisfaction Problem (CSP) approach which is not suitable for coping with undefined information. We thus propose an Interactive CSP model for reasoning on partially defined data, generating new constraints which can be used to guide the search and to incrementally process newly acquired knowledge. 1 Introduction Visual search is a very popular paradigm of computer vision, relying the location and identification of a known target in a visual scene. Many studies of human and computer-based vi..
Extending Constraint Logic Programming for Temporal Reasoning
In recent years, several constraint-based temporal reasoning frameworks have been proposed. They consider temporal points or intervals as domain elements linked by temporal constraints. Temporal reasoning in these systems is based on constraint propagation. In this paper, we argue that a language based on constraint propagation can be a suitable tool for expressing and reasoning about temporal problems. We concentrate on Constraint Logic Programming (CLP) which is a powerful programming paradigm combining the advantages of Logic Programming and the efficiency of constraint solving. However, CLP presents some limitations in dealing with temporal reasoning. First, it uses an "arc consistency" propagation algorithm which is embedded in the inference engine, cannot be changed by the user, and is too weak in many temporal frameworks. Second, CLP is not able to deal with qualitative temporal constraints. We present a general meta CLP architecture which maintains the advantages of CLP, but overcomes these two main limitations. Each architectural level is a finite domain constraint solver (CLP(FD)) that reasons about constraints of the underlying level. Based on this conceptual architecture, we extend the CLP(FD) language and we specialize the extension proposed on Vilain and Kautz's Point Algebra, on Alien's Interval Algebra and on the STP framework by Dechter, Meiri and Pearl. In particular, we show that we can cope effectively with disjunctive constraints even in an interval-based framework
A system for measuring function points from an ER-DFD specification
We present a tool for measuring the Function Point software metric from the specification of a software system expressed in the form of an Entity Relationship diagram plus a Data Flow Diagram (ER-DFD). First, the informal and general Function Point counting rules are translated into rigorous rules expressing properties of the ER-DFD. Then, the rigorous rules are translated into Prolog. The measures given by the system on a number of case studies are in accordance with those of human experts
Expressing and Verifying Business Contracts with Abductive Logic Programming
In this article, we propose to adopt the SCIFF language to specify
business contracts and show how its proof procedure is useful to
verify contract execution and fulfilment. SCIFF is a declarative
language based on abductive logic programming, which accommodates
forward rules, predicate definitions, and constraints over finite
domain variables. Its declarative semantics is abductive, and can be
related to that of deontic operators; its operational specification is
a sound and complete proof procedure, defined as a set of transition
rules, which has been implemented and integrated into a reasoning and
verification tool. A variation of the SCIFF proof-procedure (g-SCIFF)
can be used for static verification of contract properties.
We demonstrate the use of the SCIFF language for business contract
specification and verification, in a concrete scenario. In order to
accommodate integration of SCIFF with architectures for business
contract, we also show the encoding of SCIFF contract rules in RuleML
Exploiting abduction for learning from incomplete interpretations
In this paper we describe an approach for integrating abduction
and induction in the ILP setting of learning from interpretations
with the aim of solving the problem of incomplete information both in the background knowledge and in the interpretations. The approach is inspired by the techniques developed in the learning from entailment setting for performing induction from an incomplete background knowledge. Similarly to those techniques, we exploit an abductive proof procedure for completing the available background knowledge and input interpretations.
The approach has been implemented in a system called AICL that is based on the ILP system ICL. Preliminary experiments have been performed on a toy domain where knowledge has been gradually removed. The experiments show that AICL has an accuracy that is superior to the one of ICL for levels of incompleteness between 5% and 25
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