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.
Abstract We describe a program for verifying that a set of rules in an expert system eomprehensively spans the knowledge of a specialized domain. The program has been devised and tested within the context of the ONCOC...
Abstract We describe a program for verifying that a set of rules in an expert system eomprehensively spans the knowledge of a specialized domain. The program has been devised and tested within the context of the ONCOCIN System, a rule-based consultant for clinical oncology The stylized format of ONCOCIN's rules has allowed the automatic detection of a number of common errors as the knowledge base has been developed This capability suggests a general mechanism for correcting many problems with knowledge base completeness and consistency before they can cause performance errors
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.
We propose a flexible frame-structured representation and agenda-based control mechanism for the construction of production-type systems. Advantages of this architecture include uniformity, control freedom, and extens...
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