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The success of diagnostic knowledge systems has been proved over the last decades. Nowadays, intelligent systems are embedded in machines within various domains or are used in interaction with a user for solving problems. However, the development of a knowledge system is still a critical issue. Similarly to projects dealing with customized software at a highly innovative level a precise specification often cannot be given in advance. Moreover, necessary requirements of the knowledge system can be defined not until the project has been started or are changing during the development phase. This thesis motivates that classical, document-centered approaches cannot be applied in such a setting. We introduce an agile process model for developing diagnostic knowledge systems, mainly inspired by the ideas of the eXtreme Programming methodology known in software engineering. The engineering process is supported at a primary level by the introduction of knowledge containers, that define an organized view of knowledge contained in the system. The actual knowledge is acquired and formalized right from start, and the integration to runnable knowledge systems is done continuously in order to allow for an early and concrete feedback. The validity and maintainability of the collected knowledge is ensured by appropriate test methods and restructuring techniques, respectively. Additionally, we propose learning methods to support the knowledge acquisition process sufficiently. The process model and its activities are evaluated in two real life applications: in a medical and in an environmental project the benefits of the agile development are clearly demonstrated.
The central themes of the 14th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2004) were ontological engineering and the Semantic Web. These provide the key foundational and delivery mechanisms for building open, Web-based knowledge services. However, consistent with the tradition of EKAW conferences, EKAW 2004 was concerned with all aspects of eliciting, acquiring, modelling and managing knowledge, and its role in the construction of knowledge-intensive systems. Indeed a key aspect of the Knowledge Acquisition Workshops (KAWs) held in the US, Europe and Asia over the past 20 years has been the emphasis on ‘holistic’ knowledge engineering, addressing problem solving, usability, socio-technological factors and knowledge modelling, rather than simply analyzing and designing symbol-level inferential mechanisms. The papers included in this volume are thus drawn from a variety of research areas both at the cutting edge of research in ontologies and the Semantic Web and in the more traditionally grounded areas of knowledge engineering. A Semantic Web service can be seen as the addition of semantic technologies to Web services to produce Web-accessible services that can be described using appropriate ontologies, reasoned about and combined automatically. Since Web services can be seen as Web-accessible computational objects, much of the work in this area is also concerned with problem-solving methods (PSMs).
The book presents a representative selection of all publications published between 01/2009 and 06/2010 in various books, journals and conference proceedings by the researchers of the institute cluster: IMA - Institute of Information Management in Mechanical Engineering ZLW - Center for Learning and Knowledge Management IfU - Institute for Management Cybernetics, Faculty of Mechanical Engineering, RWTH Aachen University The contributions address the cluster's five core research fields: suitable processes for knowledge- and technology-intensive organizations, next-generation teaching and learning concepts for universities and the economy, cognitive IT-supported processes for heterogeneous and cooperative systems, target group-adapted user models for innovation and technology development processes, semantic networks and ontologies for complex value chains and virtual environments Innovative fields of application such as cognitive systems, autonomous truck convoys, telemedicine, ontology engineering, knowledge and information management, learning models and technologies, organizational development and management cybernetics are presented. The contributions show the unique potential of the broad and interdisciplinary research approach of the ZLW/IMA and the IfU.
​This book covers the theory, design and applications of computer networks, distributed computing and information systems. Networks of today are going through a rapid evolution, and there are many emerging areas of information networking and their applications. Heterogeneous networking supported by recent technological advances in low-power wireless communications along with silicon integration of various functionalities such as sensing, communications, intelligence and actuations is emerging as a critically important disruptive computer class based on a new platform, networking structure and interface that enable novel, low-cost and high-volume applications. Several of such applications have been difficult to realize because of many interconnections problems. To fulfill their large range of applications, different kinds of networks need to collaborate, and wired and next-generation wireless systems should be integrated in order to develop high-performance computing solutions to problems arising from the complexities of these networks. The aim of the book “Advanced Information Networking and Applications” is to provide latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to the emerging areas of information networking and applications.
"This book provides a comprehensive collection of state-of-the-art advancements in rule languages"--Provided by publisher.
This book constitutes the thoroughly refereed post-proceedings of the 29th Annual German Conference on Artificial Intelligence, KI 2006, held in Bremen, Germany, in June 2006. This was co-located with RoboCup 2006, the innovative robot soccer world championship, and with ACTUATOR 2006, the 10th International Conference on New Actuators. The 29 revised full papers presented together with two invited contributions were carefully reviewed and selected from 112 submissions.
Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the designing of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.
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Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.