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UMFE - A USER MODELING FRONT-END SUBSYSTEM

INTERNATIONAL JOURNAL OF MAN-MACHINE STUDIES 1985年第1期23卷 71-88页

作者： SLEEMAN, D STANFORD UNIV DEPT COMP SCIHEURISTIC PROGRAMMING PROJECTSTANFORDCA 94305 USA

The paper argues that user models are an essential component of any system which attempts to be “user friendly”, and that expert systems should tailor explanations to their users, be they super-experts or novices. In particular, this paper discusses a data-driven user modelling front-end subsystem, UMFE, which assumes that the user has asked a question of the main system (e.g. an expert system, intelligent tutoring system etc.), and that the system provides a response which is passed to UMFE. UMFE determines the user's level of sophistication by asking as few questions as possible, and then presents a response in terms of concepts which UMFE believes the user understands. Investigator-defined inference rules are then used to suggest additional concepts the user may/may not know, given the concepts the user indicated he or she knew in earlier questioning. Several techniques are discussed for detecting and removing inconsistencies in the user model. Additionally, UMFE modifies its inference rules for individual users when it detects certain types of inconsistencies. UMFE is a portable domain-independent implementation of a system which infers overlay models for users. UMFE has been used in conjunction with NEOMYCIN; and the paper contains several protocols which demonstrate its principal features. The paper concludes with a critique of UMFE and suggestions for enhancing the current system.

关键词： TEACHING MACHINES

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KNOWLEDGE ENGINEERING - THE APPLIED SIDE OF ARTIFICIAL-INTELLIGENCE

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ANNALS OF THE NEW YORK ACADEMY OF SCIENCES 1984年第NOV期426卷 91-107页

作者： FEIGENBAUM, EA Heuristic Programming Project Computer Science Department Stanford University Stanford California 94305

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STRATEGIC EXPLANATIONS FOR A DIAGNOSTIC CONSULTATION SYSTEM

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INTERNATIONAL JOURNAL OF MAN-MACHINE STUDIES 1984年第1期20卷 3-19页

作者： HASLING, DW CLANCEY, WJ RENNELS, G Heuristic Programming Project Computer Science Department Stanford University Stanford California 94305 U.S.A.

The problem of automatic explanation of reasoning, especially as it relates to expert systems is examined. Explanation means the ability of a program to discuss what it is doing in some understandable way. A general framework in which to view explanation and review some of the research done in this area is presented. The explanation system for NEOMYCIN, a medical consultation program is studied. A consultation program interactively helps a user to solve a problem. Our goal is to have NEOMYCIN explain its problem-solving strategies. An explanation of strategy describes the plan the program is using to reach a solution. Such an explanation is usually concrete, referring to aspects of the current problem situation. Abstract explanations articulate a general principle, which can be applied in different situations;such explanations are useful in teaching and in explaining by analogy. The aspects of NEOMYCIN that make abstract strategic explanations possible, the representation of strategic knowledge explicitly and separately from domain knowledge and demonstrate how this representation can be used to generate explanations, is described.

关键词： SYSTEMS SCIENCE AND CYBERNETICS

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KNOWLEDGE ACQUISITION FOR CLASSIFICATION EXPERT SYSTEMS. 84

KNOWLEDGE ACQUISITION FOR CLASSIFICATION EXPERT SYSTEMS.

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Proceedings of the 1984 Annual Conference of the Association for Computing Machinery: The Fifth Generation Challenge.

作者： Clancey, William J. Stanford Univ Heuristic Programming Project Stanford CA USA Stanford Univ Heuristic Programming Project Stanford CA USA

ISBN: (纸本)089791144X

Expert systems are generally described by a mixture of terms that confuse implementation language with knowledge structure and the search process. This confusion makes it difficult to analyze new problems and to derive a set of knowledge engineering principles. A rigorous, logical description of expert systems reveals that a small set of terms and relations can be used to describe many rule-based expert systems. In particular, one common method for solving problems is by classification - heuristically relating data abstractions to a preenumerated network of solutions. This model can be used as a framework for knowledge acquisition, particularly in the early stages for organizing the expert's vocabulary and decomposing problems.

关键词： Systems science and cybernetics

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Adapting a consultation system to critique user plans

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Journal of Medical Systems 1984年第3期8卷 215-216页

作者： Langlotz, Curtis P. Heuristic Programming Project Departments of Medicine and Computer Science Stanford University Stanford

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Signal-to-symbol transformation: Reasoning in the HASP/SIAP program

Signal-to-symbol transformation: Reasoning in the HASP/SIAP ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： H. Nii Heuristic Programming Project Computer Science Department University of Stanford USA

In the past fifteen years, artificial intelligence scientists have built several signal interpretation, or understanding, programs. These programs have combined "low" level signal processing algorithms with knowledge representation and reasoning techniques used in knowledge-based, or expert, systems. [4] They have shown how the use of task domain knowledge combined with symbolic manipulation techniques can be of use in making signal understanding systems more effective and efficient. HASP/SIAP is one such program that tries to interpret the meaning of passively collected sonar data. In this paper we explore some of the AI techniques that contribute in the "understanding" process. We also describe the organization of HASP/SIAP system as an example of a programming framework that show promise for applications in a class of similar problems.

关键词： Marine vehicles Sonogram Frequency Oceans Displays Sea measurements Acoustic noise Sonar equipment Monitoring Propellers

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THE SCIENCE OF BIOMEDICAL COMPUTING

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MEDICAL INFORMATICS 1984年第3-4期9卷 185-193页

作者： SHORTLIFFE, EH Heuristic Programming Project Departments of Medicine and Computer Science Stanford University School of Medicine Stanford CA 94305 United States

1. Introduction This is a remarkably exciting time to be involved professionally in the field of medical informatics. The underlying scientific principles are beginning to be identified and defined, educators are increasingly acknowledging the importance of the field for physicians of the present and future, and the technology itself is growing at rates that make the future of the field both unbounded and impossible to predict. One has the sense that what was once a field for pioneers is now reaching the stage of established settlements, with a history, traditions, and a feel of permanence. © 1984 Informa UK Ltd All rights reserved: reproduction in whole or part not permitted.

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CLASSIFICATION PROBLEM SOLVING.

CLASSIFICATION PROBLEM SOLVING.

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Proceedings of the National Conference on Artificial Intelligence, AAAI-84.

作者： Clancey, William J. Stanford Univ Heuristic Programming Project Stanford CA USA Stanford Univ Heuristic Programming Project Stanford CA USA

A broad range of heuristic programs - embracing forms of diagnosis, catalog selection, and skeletal planning - accomplish a kind of well-structured problem solving called classification. These programs have a characteristic inference structure that systematically relates data to a pre-enumerated set of solutions by abstraction, heuristic association, and refinement. This level of description specifies the knowledge needed to solve a problem, independent of its representation in a particular computer language. The classification problem-solving model provides a useful framework for recognizing and representing similar problems, for designing representation tools, and for understanding why non-classification problems require different problem-solving methods.

关键词： Information science

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THE EPISTEMOLOGY OF A RULE-BASED EXPERT SYSTEM - A FRAMEWORK FOR EXPLANATION

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ARTIFICIAL INTELLIGENCE 1983年第3期20卷 215-251页

作者： CLANCEY, WJ Heuristic Programming Project Computer Science Department Stanford University Stanford CA 94305 U.S.A

Production rules are a popular representation for encoding heuristic knowledge in programs for scientific and medical problem solving. However, experience with one of these programs, mycin, indicates that the representation has serious limitations: people other than the original rule authors find it difficult to modify the rule set, and the rules are unsuitable for use in other settings, such as for application to teaching. These problems are rooted in fundamental limitations in mycin"s original rule representation: the view that expert knowledge can be encoded as a uniform, weakly structured set of if/then associations is found to be wanting. To illustrate these problems, this paper examines mycin"s rules from the perspective of a teacher trying to justify them and to convey a problem-solving approach. We discover that individual rules play different roles, have different kinds of justifications, and are constructed using different rationales for the ordering and choice of premise clauses. This design knowledge, consisting of structural and strategic concepts which lie outside the representation, is shown to be procedurally embedded in the rules. Moreover, because the data/hypothesis associations are themselves a proceduralized form of underlying disease models, they can only be supported by appealing to this deeper level of knowledge. Making explicit this structural, strategic and support knowledge enhances the ability to understand and modify the system.

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Data abstraction in GLISP 83

Data abstraction in GLISP

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1983 ACM SIGPLAN Symposium on programming Language Issues in Software Systems, SIGPLAN 1983

作者： Novak, Gordon S. Heuristic Programming Project Computer Science Department Stanford University StanfordCA94305 United States

ISBN: (纸本)0897911083

GLISP is a high-level language that is based on Lisp and is compiled into Lisp. It provides a versatile abstract-data-type facility with hierarchical inheritance of properties and object-centered programming. The object code produced by GLISP is optimized, so that it is about as efficient as handwritten Lisp. An integrated programming environment is provided, including editors for programs and data-type descriptions, interpretive programming features, and a display-based inspector/editor for data. GLISP is fully implemented. © 1983 ACM.

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