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INTEGRATING knowledge-BASED SYSTEMS AND DATABASES

CLINICA CHIMICA ACTA 1993年第1-2期222卷 101-115页

作者： GRIMSON, JB Knowledge and Data Engineering Group Department of Computer Science Trinity College Dublin 2 Ireland

In recent years, there has been an explosion of interest among the computing community in the field of artificial intelligence, particularly in the areas of natural language processing and knowledge-based systems (KBS). The medical domain has seen the development of hundreds of KBSs and there is substantial evidence to show that the application of a knowledge-based approach to decision support can go a long way towards overcoming the information overload experienced by many clinicians today. Yet many of these medical KBSs are still at the prototype stage and are mainly confined to research laboratories. There are many reasons for this apparently slow take-up of the technology, but one of the most significant is the lack of integration into the regular routine information processing of the organisation, in particular the database processing. This paper discusses the benefits of such integration and methods for achieving it in the context of general trends in information systems. Database technology provides efficient and secure management of large amounts of data in a multi-user, multiapplication environment. knowledge-based technology, on the other hand, provides mechanisms for building intelligent systems. Thus, for example, given a set of facts about a domain (symptoms, laboratory test results, etc.) together with a set of rules which apply to that domain (e.g.'if TT4 > 150 nmol/l then suspect hyperthyroidism'), a KBS can deduce new information about that domain automatically. The effective integration of these two technologies is seen as a means of achieving the intelligent information systems of the future. There are three basic approaches to integrating KBSs and databases. The first is to start with the KBS and incorporate data management functions. Alternatively, intelligence from the KBS can be incorporated into the database. Finally, the two systems can be allowed to coexist as independent systems which can talk to each other by means of standard interfaces.

关键词： DATABASE MANAGEMENT knowledge-BASED SYSTEMS EXPERT SYSTEMS

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QStruc. An approach for qualitative structural analysis

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Computing Systems in engineering: An International Journal 1993年第2-3期4卷 147-147页

作者： Fruchter, R. Law, K.H. Iwasaki, Y. Center for Integrated Facility Engineering Department of Civil Engineering Stanford University Stanford U.S.A. Department of Civil Engineering Stanford University Stanford U.S.A. Knowledge System Laboratory Department of Computer Science Stanford University Stanford U.S.A.

Understanding the behavior of a structure and its components in the preliminary design phase is important since decisions made at this time can have a significant influence on the quality of the final design. It can also potentially reduce the number of alternative solutions and avoid costly design revisions. However, there are few tools available for modeling of a structure and performing qualitative analysis and interpretation of a conceptual design. We describe a framework QStruc for qualitative structural analysis which combines first principles in structural engineering and experimental knowledge of structural behavior. The purpose of QStruc is to generate refined qualitative models and to infer the qualitative response of a given preliminary structure. The results are expressed by qualitative deflected shape, moments and reactions. The proposed qualitative analysis strategy is a “greedy” depth-first algorithm that tries to expand the response of behavior as much as possible from known parameter values. This strategy makes use of a causal ordering mechanism, qualitative calculus and Quantity Lattice (R. Simmons, “Commonsense arithmetic reasoning” in Proceedings of the AAAI86 , pp. 118–124, Morgan Kaufmann, San Mateo, California, 1986) to reason about partial ordering among physical parameters.

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Efficient postprocessing algorithms for error correction in handwritten Hangul address and human name recognition

Efficient postprocessing algorithms for error correction in ...

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International Conference on Document Analysis and Recognition

作者： S.-W. Lee E.-S. Kim B.-W. Min Department of Computer Science Chungbuk National University Cheonghu Chungbuk South Korea Knowledge-Based Systems Laboratory Systems Engineering Research Institute Daejeon South Korea

Proposes efficient postprocessing algorithms for error correction in handwritten Hangul (Korean script) address and human name recognition. As the load on the character recognizer for the recognition of the administrative district part in addresses was reduced by restricting the candidate characters to be matched based on a hierarchical address lexicon, the processing speed and recognition rate were greatly improved. Also, the misrecognition results from the character recognizer were corrected by using efficient postprocessing algorithms based on backtracking. For the recognition of the human name part, misrecognition of human names could be effectively corrected by combining the a priori probability and the confusion probability of each character making up the human names.< >

关键词： Error correction Humans Character recognition Handwriting recognition Buildings Character generation Cities and towns computer science knowledge based systems Laboratories

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 1993年 689 LNAI卷 V-VI页

作者： Komorowski, Jan Raś, Zbigniew W. Knowledge Systems Group Faculty of Computer Science and Electrical Engineering The Norwegian Institute of Technology The University of Trondheim TrondheimN-7034 Norway Department of Computer Science University of North Carolina CharlotteNC28223 United States

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A Study of How Domain knowledge Improves knowledge-Based Learning Systems 8

A Study of How Domain Knowledge Improves Knowledge-Based Lea...

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8th International Workshop on Machine Learning, ICML 1991

作者： Whitehall, Bradley L. Lu, Stephen C-Y. Knowledge-Based Engineering Systems Research Laboratory University of Illinois UrbanaIL61801 United States Department of Mechanical and Industrial Engineering Beckman Institute for Advanced Science and Technology United States Department of Computer Science Beckman Institute for Advanced Science and Technology United States

ISBN: (纸本)1558602003

A knowledge-based learning system is developed to demonstrate that intelligently selecting a subset of examples based on domain knowledge for rule induction can be more productive than using all provided examples. knowledge-based learning uses an incomplete domain theory and examples to induce knowledge missing in the theory. knowledge-based learning systems determine where knowledge is missing in a domain theory, select a relevant subset of examples to use in induction, and induce new rules or modify existing rules. © ICML *** rights reserved

关键词： Learning systems

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Preface

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 1991年 498 LNCS卷 3-3页

作者： Andersen, Rudolf Bubenko, Janis A. Sølvberg, Arne Information and Knowledge Systems Group Department of Electrical Engineering and Computer Science The Norwegian Institute of Technology TrondheimN-7034 Norway SISU-Swedish Institute for Systems Development Box 1250 KistaS-164 28 Sweden

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ROLE OF SIMULATION IN RAPID PROTOTYPING FOR CONCEPT DEVELOPMENT

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NAVAL ENGINEERS JOURNAL 1991年第3期103卷 204-211页

作者： KING, JF BARTON, DE J. Fred King:is the manager of the Advanced Technology Department for Unisys in Reston Virginia. He earned his Ph.D. in mathematics from the University of Houston in 1977. He has been principal investigator of research projects in knowledge engineering pattern recognition and heuristic problem-solving. Efforts include the development of a multi-temporal multispectral classifier for identifying graincrops using LANDSAT satellite imagery data for NASA. Also as a member of the research team for a NCI study with Baylor College of Medicine and NASA he helped develop techniques for detection of carcinoma using multispectral microphotometer scans of lung tissue. He established and became technical director of the AI Laboratory for Ford Aerospace where he developed expert scheduling modeling and knowledge acquisition systems for NASA. Since joining Unisys in 1985 he has led the development of object-oriented programming environments blackboard architectures data fusion techniques using neural networks and intelligent data base systems. Douglas E. Barton:is manager of Logistics Information Systems for Unisys in Reston Virginia. He earned his B.A. degree in computer science from the College of William and Mary in 1978 and did postgraduate work in London as a Drapers Company scholar. Since joining Unisys in 1981 his work has concentrated on program management and software engineering of large scale data base management systems and design and implementation of knowledge-based systems in planning and logistics. As chairman of the Logistics Data Subcommittee of the National Security Industrial Association (NSIA) he led an industry initiative which examined concepts in knowledge-based systems in military logistics. His responsibilities also include evaluation development and tailoring of software engineering standards and procedures for data base and knowledge-based systems. He is currently program manager of the Navigation Information Management System which provides support to the Fleet Ballistic Missile Progr

A valuable technique during concept development is rapid prototyping of software for key design components. This approach is particularly useful when the optimum design approach is not readily apparent or several known alternatives need to be rapidly evaluated. A problem inherent in rapid prototyping is the lack of a "target system" with which to interface. Some alternatives are to develop test driver libraries, integrate the prototype with an existing working simulator, or build one for the specific problem. This paper presents a unique approach to concept development using rapid prototyping for concept development and scenario-based simulation for concept verification. The rapid prototyping environment, derived from artificial intelligence technology, is based on a blackboard architecture. The rapid prototype simulation capability is provided through an object-oriented modeling environment. It is shown how both simulation and blackboard technologies are used collectively to rapidly gain insight into a tenacious problem. A specific example will be discussed where this approach was used to evolve the logic of a mission controller for an autonomous underwater vehicle.

关键词： Military engineering

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A parallel implementation of collective learning systems theory: Adaptive Learning Image Analysis System (ALIAS) 90

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Proceedings of the 1990 ACM annual conference on Cooperation

作者： Peter Bock The University of Ulm Research Institute for Applied Knowledge processing (FAW) Ulm West Germany and Department of Electrical Engineering and Computer Science The George Washington University Washington DC

ISBN: (纸本)9780897913485

An alternative to preprogrammed rule-based Artificial Intelligence is a hierarchical network of cellular automata which acquire their knowledge through learning based on a series of trial-and-error interactions with an evaluating Environment, much as humans do. The input to the hierarchical network is provided by a set of sensors which perceive the external world. Based upon this perceived information and past experience (memory), the learning automata synthesize collections of trial responses. Periodically the automata estimate the effectiveness of these collections using either internal evaluations (unsupervised learning) or external evaluations from the Environment (supervised learning), modifying their memories accordingly. Known as Collective Learning Systems Theory, this paradigm has been applied to many sophisticated gaming problems, demonstrating robust learning and dynamic *** on a versatile architecture for massively parallel networks of processors for Collective Learning Systems, a Transputer-based parallel-processing image processing engine comprising 32 learning cells and 32 non-learning cells has been applied to a sophisticated image processing task: the scale-invariant and translation-invariant detection of anomalous features in otherwise “normal” images. In cooperation with Robert Bosch GmbH, this engine is currently being constructed and tested under the direction of the author at the Research Institute for Applied knowledge Processing (FAW-Ulm) as Project ALIAS: Adaptive Learning Image Analysis System. Initial results indicate excellent detection, discrimination, and localization of anomalies.

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Induction of Decision Trees from Inconclusive Data 6

Induction of Decision Trees from Inconclusive Data

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6th International Workshop on Machine Learning, ICML 1989

作者： Spangler, Scott Fayyad, Usama M. Uthurusamy, Ramasamy Knowledge Engineering Group Advanced Engineering Staff GM Technical Center WarrenMI48090 United States Artificial Intelligence Lab EECS Department The University of Michigan Ann ArborMI48109 United States Computer Science Department GM Research Laboratories GM Technical Center WarrenMI48090 United States

ISBN: (纸本)1558600361

Learning from inconclusive data is an important problem that has not been addressed in the concept learning literature. In this paper, we define inconclusiveness and illustrate why ID3-like algorithms are bound to result in overspecialized classifications when trained on inconclusive data. We address the difficult problem of deciding when to stop specialization during top-down decision tree generation, and describe a modified version of Quinlan's ID3 algorithm, called INFERULE, which addresses some of the problems involved in learning from inconclusive data. Results show that INFERULE outperformed ID3 (with and without pruning) in tests on a real-world diagnostic database containing automobile repair cases. © Proceedings of the 6th International Workshop on Machine Learning, ICML 1989.

关键词： Decision trees

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CLEER - AN AI-SYSTEM DEVELOPED TO ASSIST EQUIPMENT ARRANGEMENTS ON WARSHIPS

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NAVAL ENGINEERS JOURNAL 1989年第3期101卷 127-137页

作者： ZHOU, HH SILVERMAN, BG SIMKOL, J Dr. H. Harry Zhou:is a research professor at the Institute for Artificial Intelligence of The George Washington University. Dr. Zhou received his master's degree and Ph.D. in computer science from Vanderbilt University in 1984 and 1987 respectively. He did his dissertation in the fields of artificial intelligence analogical reasoning and machine learning. His research interests include: classifier systems genetic algorithms learning by analogy inductive learning adaptive expert systems automated knowledge acquisition and adaption. He is also interested in data base design programming languages mental modeling and software engineering. Dr. Barry G. Silverman:is director of the Institute for Artificial Intelligence and a professor at the Engineering Administration Department of The George Washington University. He is also president of IntelliTek Inc. an AI consulting firm. Dr. Silverman received the B.S.E. M.S.E. and Ph.D. degrees from the University of Pennsylvania. He has been a principal developer of four generic AI products as well as eight AI applications. Since 1979 he has written over 100 papers and reports on these AI efforts. Joel Simkol:is a research scientist currently engaged in designing expert system architectures to support electronic warfare vulnerability analyses threat assessments shipboard topside antenna arrangements and C3countermeasures employment. Mr. Simkol's work in applying expert systems technology to electromagnetic interference and to spectrum management has generated increased interest and participation from all branches of government agencies.

This paper describes a modularized AI system being built to help improve electromagnetic compatibility (EMC) among shipboard topside equipment and their associated systems. CLEER is intended to act as an easy to use integrator of existing expert knowledge and pre-existing data bases and large scale analytical models. Due to these interfaces; to the need for portability of the software; and to artificial intelligence related design requirements (such as the need for spatial reasoning, expert data base management, model base management, track-based reasoning, and analogical (similar ship) reasoning) it was realized that traditional expert system shells would be inappropriate, although relatively off-the-shelf AI technology could be incorporated. In the same vein, the rapid prototyping approach to expert system design and knowledge engineering was not pursued in favor of a rigorous systems engineering methodology. The critical design decisions affecting CLEER's development are summarized in this paper along with lessons learned to date all in terms of “how,” “why,” and “when” specific features are being developed.

关键词： Artificial Intelligence

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