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ARTIFICIAL-INTELLIGENCE LANGUAGES IN ENGINEERING-EDUCATION

INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION 1994年第2期31卷 138-143页

作者： ROWLAND, JG JAIN, LC School of Computer and Information Science Knowledge-Based Engineering Systems Group University of South Australia Adelaide Australia School of Electronic Engineering Knowledge-Based Engineering Systems Group University of South Australia Adelaide Australia

This paper looks at the two most commonly used artificial intelligence programming languages, LISP and PROLOG. The differences between conventional programming and symbolic computing and between LISP and PROLOG are pr... 详细信息

关键词： Engineering education

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Managing structured documents in distributed publishing environments 5th

Managing structured documents in distributed publishing envi...

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5th International Conference Database and Expert systems Applications, DEXA 1994

作者： Burger, Franz Quirchmayr, Gerald Reich, Siegfried Min Tjoa, A. FAW - Research Institute for Applied Knowledge Processing HagenbergA-4232 Austria University of Vienna Institute for Applied Computer Science & Information Systems Information Engineering Group ViennaA-1010 Austria

ISBN: (纸本)9783540584353

Advanced developments in the area of broadband communications, as well as database and groupware technology propose to enable more efficient cooperation in workgroups. A scenario sketching the cooperative production of innovative electronic reference works is used to derive requirements for data management. Based on these requirements an integrated architecture, consisting of workflow- and document management components, is proposed. The focus of this paper is on document management in distributed environments. Additionally, we show implementation aspects of an abstract interface for flexible handling of structured documents. © Springer-Verlag Berlin Heidelberg 1994.

关键词： information management

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Model-based interface development 94

Model-based interface development

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Conference on Human Factors in Computing systems, CHI 1994

作者： Puerta, Angel Szekely, Pedro Knowledge Systems Laboratory Medical Computer Science Group Stanford University MSOB X215 StanfordCA94305 United States Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del ReyCA90292 United States

ISBN: (纸本)0897916514

Model-based interface development is a new paradigm for developing interfaces that offers solutions to the main shortcomings of current tools. This paradigm is based on constructing a declarative description of how an interface should look and behave (model), and using the description to control the execution of an interface. This paper briefly summarizes the main shortcomings of interface development tools used today, discusses the main elements of the model-based approach, and closes with a discussion of the state of the art and the challenges that lie ahead. © 1994 ACM.

关键词： User interfaces

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Design Verification Using Functional knowledge

Design Verification Using Functional Knowledge

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1992 AAAI Fall Symposium on Design from Physical Principles

作者： Iwasaki, Y. Chandrasekaran, B. Knowledge Systems Laboratory Stanford University 701 Welch Rd. Palo AltoCA94304 United States Laboratory for AI Research Dept. of Computer & Information Science The Ohio State University 217 B.Bolz Hall 2036 Neil Avenue ColumbusOH43210-1277 United States

This paper focuses on the task of design verification using both knowledge of the structure of a device and its intended functions. In particular, it addresses the question of when one can say a behavior predicted by a prediction system achieves the desired function in the manner intended by the designer. We use Functional Representation (Sembugamoorthy & Chandrasekaran 1986) to represent the function of a device and the expected causal mechanism for achieving it. We present a formal definition of matching between a system trajectory generated by a simulation system and the description of a causal process to achieve a function expressed in Functional Representation. We believe that evaluating a behavior with respect to the expected causal process as well as the function improves the chances of uncovering hidden flaws in a design that may otherwise go undetected at an early stage. © 1992, AAAI (***). All rights reserved.

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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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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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Improving explanations in knowledge-based systems: RATIONALE

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knowledge Acquisition 1990年第4期2卷 301-343页

作者： Abu-Hakima, Suhayya Oppacher, Franz Knowledge Systems Laboratory Institute for Information Technology National Research Council of Canada Ottawa Canada K1A 0R8 USA School of Computer Science Carleton University Ottawa Canada K1S 5B6 USA

The paper describes a framework, RATIONALE, for building knowledge-based diagnostic systems that explain by reasoning explicitly. Unlike most existing explanation facilities that are grafted onto an independently designed inference engine, RATIONALE behaves as though it has to deliberate over and explain to itself, each refinement step. By treating explanation as primary, RATIONALE forces the system designer to represent knowledge explicitly that might otherwise be left implicit. This includes knowledge as to why a particular hypothesis is preferred, an exception is ignored, and a global inference strategy is chosen. RATIONALE integrates explanations with reasoning by allowing a causal and/or functional description of the domain to be represented explicitly. Reasoning proceeds by constructing a hypothesis-based classification tree whose root hypothesis contains the most general diagnosis of the system. Guided by a focusing algorithm, the classification tree branches into more specific hypotheses that explain the more detailed symptoms provided by the user. As the system is used, the classification tree also forms the basis for a dynamically generated explanation tree which holds both the successful and failed branches of the reasoning knowledge. RATIONALE is implemented in Quintus Prolog with a hypertext and graphics oriented interface under NeWS. It provides an environment for tying together the processes of knowledge acquisition, system implementation and explanation of system reasoning. © 1990 Academic Press Limited.

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TOWARD AI RESEARCH METHODOLOGY - 3 CASE STUDIES IN EVALUATION

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IEEE TRANSACTIONS ON systems MAN AND CYBERNETICS 1989年第3期19卷 634-646页

作者： COHEN, PR HOWE, AE Experimental Knowledge Systems Laboratory Department of Computer and Information Science University of Massachusetts Amherst MA USA

The roles of evaluation in empirical artificial intelligence (AI) research are described, in an idealized cyclic model and in the context of three case studies. The case studies illustrate the pitfalls in evaluation and the contributions of evaluation at all stages of the research cycle. Evaluation methods are contrasted with those of the behavioral sciences, and it is concluded that AI must define and refine its own methods. To this end, several experiment schemas and many specific evaluation criteria are described. Recommendations are offered in the hope of encouraging the development and practice of evaluation methods in AI. The first case study illustrates problems with evaluating knowledge-based systems, specifically a portfolio management expert system called FOLIO. The second study focuses on the relationship between evaluation and the evolution of the GRANT system, specifically, how the evaluations changed as GRANT's knowledge base was sealed up. Third, the cyclic nature of a given research model is examined.< >

关键词： Artificial intelligence computer aided software engineering Laboratories Context modeling Cost accounting Performance evaluation information retrieval information analysis Performance analysis Current measurement

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A method for acquiring strategic knowledge

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knowledge Acquisition 1989年第3期1卷 255-277页

作者： Gruber, Thomas R. Experimental Knowledge Systems Laboratory Computer and Information Science University of Massachusetts Amherst MA 01003 United States

In this article we present an automated method for acquiring strategic knowledge from experts. Strategic knowledge is used by an agent to decide what action to perform next, where actions effect both the agent's beliefs and the state of the external world. Strategic knowledge underlies expertise in many tasks, yet it is difficult to acquire from experts and is generally treated as an implementation problem. The knowledge acquisition method consists of the design of an operational representation for strategic knowledge, a technique for eliciting it from experts, and an interactive assistant that manages a learning dialog with the expert. The assistant elicits cases of expert-justified strategic decisions and generalizes strategic knowledge with syntactic induction guided by the expert. The knowledge acquisition method derives its power and limitations from the way in which strategic knowledge is represented and applied. © 1989 Academic Press Limited.

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'Deep' models and their relation to diagnosis

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Artificial Intelligence In Medicine 1989年第1期1卷 29-40页

作者： Chandrasekaran, B. Smith Jr., J.W. Sticklen, Jon. Laboratory for Artificial Intelligence Research Computer and Information Science Department The Ohio State University Columbus OH 43210 United States Laboratory for Knowledge-Based Medical Systems Department of Pathology The Ohio State University College of Medicine Columbus OH 43210 United States Computer Science Department Michigan State University East Lansing MI 48824 United States

In this paper we distinguish between deep models in the sense of scientific first principles and deep cognitive models where the problem solver has a qualitative symbolic representation of the system or device that accounts for how the system 'works'. We analyze diagnostic reasoning as an information processing task, identifying the generic types of knowledge (and reasoning) needed for the task to be performed adequately. If these are available, an integrated collection of generic problem solvers can produce a diagnostic conclusion. The need for deep or causal models arises when some or all of these types of knowledge are missing in the problem solver. We provide a typology of different knowledge structures and reasoning processes that play a role in qualitative or functional reasoning and elaborate on functional representations as deep cognitive models for some aspects of causal reasoning in medicine. © 1989.

关键词： diagnostic problem-solving architectures diagnostic reasoning functional reasoning knowledge compilation qualitative models

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