The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques

The paper describes VEX-93 as a hybrid environment for developing knowledge-based and problem solver systems. It integrates methods and techniques from artificial intelligence, image and signal processing and data analysis, which can be mixed. Two hierarchical levels of reasoning contains an intelligent toolbox with one upper strategic inference engine and four lower ones containing specific reasoning models: truth-functional (rule-based), probabilistic (causal networks), fuzzy (rule-based) and case-based (frames). There are image/signal processing-analysis capabilities in the form of programming languages with more than one hundred primitive functions. User-made programs are embeddable within knowledge basis, allowing the combination of perception and reasoning. The data analyzer toolbox contains a collection of numerical classification, pattern recognition and ordination methods, with neural network tools and a data base query language at inference engines's disposal. VEX-93 is an open system able to communicate with external computer programs relevant to a particular application. Metaknowledge can be used for elaborate conclusions, and man-machine interaction includes, besides windows and graphical interfaces, acceptance of voice commands and production of speech output. The system was conceived for real-world applications in general domains, but an example of a concrete medical diagnostic support system at present under completion as a cuban-spanish project is mentioned. Present version of VEX-93 is a huge system composed by about one and half millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version

A Textual Case-Based Mobile Phone Diagnosis Support System

Java Cases and Ontology Libraries Integration for Building Reasoning Infrastructures (jCOLIBRI) is a framework which makes the development of Textual Case-Based Reasoning (CBR) applications easier by providing the preprocessing of text methods, textual similarity methods and appropriate representation for textual cases which are the major techniques needed in any CBR systems. In this paper, a Mobile Phone Diagnosis Support System is presented as an extension to jCOLIBRI which accepts a problem and reasons with cases to provide a solution related to a new given problem. Experimental evaluation using some set of problems shows that the developed system predicts the solution that is relatively closer to the user given mobile phone problem. The solution also provide the user valuable advise on how to go about solving the new problem

A Review of Diagnostic Techniques for ISHM Applications

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern

geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research

8 pages, 5 figures, 3 additional files.-- Software.[Background] Bioinformatics and medical informatics are two research fields that serve the needs of different but related communities. Both domains share the common goal of providing new algorithms, methods and technological solutions to biomedical research, and contributing to the treatment and cure of diseases. Although different microarray techniques have been successfully used to investigate useful information for cancer diagnosis at the gene expression level, the true integration of existing methods into day-to-day clinical practice is still a long way off. Within this context, case-based reasoning emerges as a suitable paradigm specially intended for the development of biomedical informatics applications and decision support systems, given the support and collaboration involved in such a translational development. With the goals of removing barriers against multi-disciplinary collaboration and facilitating the dissemination and transfer of knowledge to real practice, case-based reasoning systems have the potential to be applied to translational research mainly because their computational reasoning paradigm is similar to the way clinicians gather, analyze and process information in their own practice of clinical medicine.[Results] In addressing the issue of bridging the existing gap between biomedical researchers and clinicians who work in the domain of cancer diagnosis, prognosis and treatment, we have developed and made accessible a common interactive framework. Our geneCBR system implements a freely available software tool that allows the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction for aiding diagnosis in cancer research. For biomedical researches, geneCBR expert mode offers a core workbench for designing and testing new techniques and experiments. For pathologists or oncologists, geneCBR diagnostic mode implements an effective and reliable system that can diagnose cancer subtypes based on the analysis of microarray data using a CBR architecture. For programmers, geneCBR programming mode includes an advanced edition module for run-time modification of previous coded techniques.[Conclusion] geneCBR is a new translational tool that can effectively support the integrative work of programmers, biomedical researches and clinicians working together in a common framework. The code is freely available under the GPL license and can be obtained at http://www.genecbr.org (webcite).This work is supported in part by the projects Research on Translational Bioinformatics (ref. 08VIB6) from University of Vigo and Development of computational tools for the classification and clustering of gene expression data in order to discover meaningful biological information in cancer diagnosis (ref. VA100A08) from JCyL (Spain). The work of D. Glez-Peña is supported by a "María Barbeito" contract from Xunta de Galicia.Peer reviewe

Case-based reasoning as a decision support system for cancer diagnosis: A case study

Microarray technology can measure the expression levels of thousands of genes in an experiment. This fact makes the use of computational methods in cancer research absolutely essential. One of the possible applications is in the use of Artificial Intelligence techniques. Several of these techniques have been used to analyze expression arrays, but there is a growing need for new and effective solutions. This paper presents a Case-based reasoning (CBR) system for automatic classification of leukemia patients from microarray data. The system incorporates novel algorithms for data mining that allow filtering, classification, and knowledge extraction. The system has been tested and the results obtained are presented in this paper

A global workspace framework for combined reasoning

Artificial Intelligence research has produced many effective techniques for solving a wide range of problems. Practitioners tend to concentrate their efforts in one particular problem solving paradigm and, in the main, AI research describes new methods for solving particular types of problems or improvements in existing approaches. By contrast, much less research has considered how to fruitfully combine different problem solving techniques. Numerous studies have demonstrated how a combination of reasoning approaches can improve the effectiveness of one of those methods. Others have demonstrated how, by using several different reasoning techniques, a system or method can be developed to accomplish a novel task, that none of the individual techniques could perform. Combined reasoning systems, i.e., systems which apply disparate reasoning techniques in concert, can be more than the sum of their parts. In addition, they gain leverage from advances in the individual methods they encompass. However, the benefits of combined reasoning systems are not easily accessible, and systems have been hand-crafted to very specific tasks in certain domains. This approach means those systems often suffer from a lack of clarity of design and are inflexible to extension. In order for the field of combined reasoning to advance, we need to determine best practice and identify effective general approaches. By developing useful frameworks, we can empower researchers to explore the potential of combined reasoning, and AI in general. We present here a framework for developing combined reasoning systems, based upon Baars’ Global Workspace Theory. The architecture describes a collection of processes, embodying individual reasoning techniques, which communicate via a global workspace. We present, also, a software toolkit which allows users to implement systems according to the framework. We describe how, despite the restrictions of the framework, we have used it to create systems to perform a number of combined reasoning tasks. As well as being as effective as previous implementations, the simplicity of the underlying framework means they are structured in a straightforward and comprehensible manner. It also makes the systems easy to extend to new capabilities, which we demonstrate in a number of case studies. Furthermore, the framework and toolkit we describe allow developers to harness the parallel nature of the underlying theory by enabling them to readily convert their implementations into distributed systems. We have experimented with the framework in a number of application domains and, through these applications, we have contributed to constraint satisfaction problem solving and automated theory formation

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version