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 f...
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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.
The Arcturus system demonstrates several important principles that will characterize advanced Ada programming support environments. These include conceptual simplicity, tight coupling of tools, and effective command a...
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This paper describes the principles underlying an efficient implementation of a lazy functional language, compiling to code for ordinary computers. It is baaed on combinator-like graph reduction: the user defined func...
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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 ...
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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.
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 represen...
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.
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 obje...
GLISP is a high-level language that is compiled into LISP It provides a versatile abstract-data-type facility with hierarchical inheritance of properties and object-centered programming GLISP programs are shorter and ...
GLISP is a high-level language that is compiled into LISP It provides a versatile abstract-data-type facility with hierarchical inheritance of properties and object-centered programming GLISP programs are shorter and more readable than equivalent LISP programs The object code produced by GLISP is optimized, making it about as efficient as handwritten LISP An integrated programming environment is provided, including automatic incremental compilation, interpretive programming features, and an intelligent display-based inspector/editor for data and data-type descriptions GLISP code is relatively portable; the compiler and the data inspector are implemented for most major dialects of LISP and are available free or at nominal cost
作者：
LENAT, DBComputer Science Department
Stanford University Stanford CA 94305 U.S.A.[∗]The author is an assistant professor of Computer Science at Stanford University a member of that university"s Heuristic Programming Project and a consultant for CIS at XEROX PARC.
Builders of expert rule-based systems attribute the impressive performance of their programs to the corpus of knowledge they embody: a large network of facts to provide breadth of scope, and a large array of informal ...
Builders of expert rule-based systems attribute the impressive performance of their programs to the corpus of knowledge they embody: a large network of facts to provide breadth of scope, and a large array of informal judgmental rules (heuristics) which guide the system toward plausible paths to follow and away from implausible ones. Yet what is the nature of heuristics? What is the source of their power? How do they originate and evolve? By examining two case studies, the am and eurisko programs, we are led to some tentative hypotheses: Heuristics are compiled hindsight, and draw their power from the various kinds of regularity and continuity in the world; they arise through specialization, generalization, and—surprisingly often—analogy. Forty years ago, Polya introduced Heuretics as a separable field worthy of study. Today, we are finally able to carry out the kind of computation-intensive experiments which make such study possible.
This paper sets some context, raises issues, and provides our initial thinking on the characteristics of effective rapid prototyping techniques. After discussing the role rapid prototyping techniques can play in the s...
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ISBN:
(纸本)089791094X
This paper sets some context, raises issues, and provides our initial thinking on the characteristics of effective rapid prototyping techniques. After discussing the role rapid prototyping techniques can play in the software lifecycle, the paper looks at possible technical approaches including: heavily parameterized models, reusable software, rapid prototyping languages, prefabrication techniques for system generation, and reconfigurable test harnesses. The paper concludes that a multi-faceted approach to rapid prototyping techniques is needed if we are to address a broad range of applications successfully - no single technical approach suffices for all potentially desirable applications.
Summary Eurisko is an AI program that learns by discovery We are applying Eurisko to the task of inventing new kinds of three-dimensional microelectronic devices that can then be fabricated using recently developed la...
Summary Eurisko is an AI program that learns by discovery We are applying Eurisko to the task of inventing new kinds of three-dimensional microelectronic devices that can then be fabricated using recently developed laser recrystallization techniques Three experiments have been conducted, and some novel designs and design rules have emerged.
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