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Chowdhury S I, 1987. “State of the art in statistical expert systems” in 7th International Workshop: Expert Systems & Their Applications, Avignon: France. (This paper has half page summaries of 17 previous papers).
Google Scholar
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Gale W A, (ed.) 1986a. Artificial Intelligence and StatisticsReading, Massachusetts: Addison Wesley. This is the first book published in the field. It is a logical starting point for learning about the field.
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Gale W A, 1986e. “Overview,” Chapter 1 of Artificial Intelligence and Statistics, Reading, Massachusetts. Addison Wesley. The chapter reviews all papers that I knew about at the time, because they were only available in conference proceedings.
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Hahn G J, 1985. “More intelligent statistical software and statistical expert systems: future directions” The American Statistician39 pp. 1–16. This was the first major review. It is directed to statisticians, and contains suggestions for research directions.
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Hand D J, 1986b. “Expert systems in statistics” The Knowledge Engineering Review1 pp. 2–10. Hand's papers have typically a considerable theoretical framework to which their substance is related. This review paper is no exception, doing a nice job of describing aspects of the statistical domain that the KE should appreciate.
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Aikins J S, 1983. “Prototypical knowledge for expert systems,” Artificial Intelligence120 pp. 163–210. This paper describes Centaur, Aikins' thesis project. It provided a single framework that combined both rules and frames.
Google Scholar
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de Kleer J and Brown J S, 1985. “A Qualitative physics based on confluences,” In: Hobbs and Moore, 1985 pp. 109–194. This paper introduces a calculus of directions, and applies it in a few examples.
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Gale W A, 1986d. “Knowledge-based knowledge acquisition for a statistical consulting system,” International Journal of Man-Machine Studies26 pp. 55–64. This paper describes the abstract knowledge engineering principles on which student is based.
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Gale W A and Lubinsky D, 1986. “A comparison of representations for statistical strategies” In: Proceedings of the American Statistical Association, Statistical Computing Section, Arlington, Virginia: American Statistical Association, pp. 88–96. The paper compares the knowledge representations used by Student and TESS.
Google Scholar
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Hand D J, 1986a. “Patterns in statistical strategy” In: Artificial Intelligence and Statistics, Reading, Massachusetts: Addison Wesley pp. 355–388. This paper gives a theoretical view of the task undertaken by the various systems referenced below under strategy of data analysis.
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Hayes P, 1985. “Naive physics I: ontology for liquids” In: Formal Theories of the Commonsense World, Hobbs and Moore 1985. Eds, New Jersey: Ablex, Norwood, pp. 71–108. The paper introduces a formalization for the behaviour of liquids.
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Hobbs J, and Moore R, 1985. Formal Theories of the Commonsense World, New Jersey: Ablex, Norwood. This book is a good introduction to building formal theories in areas not previously formalized.
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|
|
Newell A, 1981. “The knowledge level” AI Magazine2(2) pp. 1–20. His presidential address introduced an idea that has organized further research.
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O'Keefe R, 1985. Logic and Lattices for A Statistical Advisor, Thesis, University of Edinburgh. This thesis describes a system that selects statistical analysis techniques but the greater value is a beginning on a formalization of data description and experiment design.
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Oldford W and Peters S, 1986a. “Implementation and study of statistical strategy” In: Artificial Intelligence and Statistics, Gale N A, ed. Reading, Massachusetts: Addison-Wesley, pp. 335–353. This paper gave a theoretical view of strategy for data analysis that is still useful to read.
Google Scholar
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Oldford W and Peters S, 1986c. “Object oriented data representations for statistical data analysis” In COMPSTAT 1986: Proceedings in Computational Statistics, De Antoni F, Lauro N and Rizzi A, (eds.) Vienna: Physica-Verlag pp. 301–308. This paper describes the knowledge representation ideas behind DINDE.
Google Scholar
|
|
Piaget J and Inhelder B, 1951. La genèse de L'idée de hasard chez l'enfant, Presses Universitaires de France: translated by Leake, Burrell, and Fishbein, 1975, The Origin of the Idea of Chance in Children, New York, Norton. A book that describes experiments showing the developmental stages of ideas of randomness and order.
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Anderson J, Farrell R and Sauers R, 1984. “Learning to program in LISP,” Cognitive Science8 pp. 87–129. The paper describes an intelligent tutoring system which taught some basics of programming in LISP.
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Barzilay A, 1984. An Expert System for Tutoring Probability Theory, Ph.D. Thesis submitted to Graduate School of Business, University of Pittsburgh. The system SPIRIT was built using OPS5. It tutors in conditional probabilities.
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Blum R L, 1982. Discovery and Representation of Causal Relationships from a Large Time-Oriented Clinical Database: The RX Project, New York: Springer-Verlag. The book is based on Blum's thesis. It describes the whole RX system.
Google Scholar
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Dambroise E, 1987. Muse: Multivariate Expertise. Thesis INRA, 9 Place Pierre Viala, 34060 Montpelier Cedex, France: an abbreviated report in English is available in: Dambroise E and Massotte, P “Muse, an expert system in statistics” In: COMPSTAT 1986: Proceedings in Computational Statistics, De Antoni, F, Lauro, N and Rizzi, A, (eds.) Vienna: Physica-Verlag, pp. 271–276. The thesis provides Dambroise with enough space to explain MUSE in detail, but the paper is much easier to get.
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Haaland P D, Yen D and Liddle R F, 1986. “An expert system for experimental design” In: Proceedings of the Statistical Computing Section, Arlington, Virginia: American Statistical Association, pp. 78–87. The paper describes a system which helps an industrial engineer select an experimental design.
Google Scholar
|
|
Portier K M and Lai P, 1983. “A statistical expert system for analysis determination” Proceedings of the Statistical Computing Section, Arlington, Virginia: American Statistical Association, pp. 309–311. This paper describes the behaviour of a system based on a decision tree.
Google Scholar
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|
Stefik M J, 1980. “Planning with constraints” Report No. 80–784, Computer Science Department, Stanford University, (Doctoral thesis). This thesis shows how knowledge about molecular genetics can be used to constrain plans and reduce an intractable search space.
Google Scholar
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|
Sussman G J, 1975. A Computer Model of Skill Acquisition, New York: Elsevier. This thesis described a program that would learn to write better programs by analyzing bugs in its programs.
Google Scholar
|
|
Weiner J M, Horowitz R and Bauer M, 1987. “An expert system for analysis of clinical trial data” In: Heiberger R, (Ed.) Computer Science and Statistics, Proceedings of the Nineteenth Symposium on the Interface, North-Holland. This paper is not so much on an expert system as on the person/machine organization of the large body of knowledge required to design ethical, legal, and knowledge enhancing experiments on treatment of diseases.
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|
Berzuini C, Ross G and Larizza C, 1986. “Developing intelligent software for non-linear model fitting as an expert system” In: COMPSTAT 1986: Proceedings in Computational Statistics, De Antoni F, Lauro N and Rizzi A, (Eds.) Vienna: Physica-Verlag, pp. 259–264. This is a first paper on a project to make an intelligent interface to the “Maximum likelihood program,” a program encoding an advanced statistical procedure.
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|
|
Carlsen F and Heuch I, 1986. “Express—an expert system utilizing standard statistical packages” In: COMPSTAT 1986: Proceedings in Computational Statistics, De Antoni F, Lauro N and Rizzi A, (Eds.) Vienna: Physica-Verlag, pp. 265–270. This system compared the locations of two samples. It used BMDP for calculations, and introduced simulation as a means of testing the system.
Google Scholar
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|
Darius P, 1986. “Building expert systems with the help of existing statistical software” In: COMPSTAT 1986: Proceedings in Computational Statistics, De Antoni F, Lauro N and Rizzi A, (Eds.) Vienna: Physica-Verlag, pp. 277–282. Darius used SAS as the programming language for a rule interpreter, and used that to select a test for comparing two group means.
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Drapier P, 1987. “Le système de régression assistée par ordinateur: RAO” preprint, 20 Rue Rouget de L'Isle, 94100 Saint Maur, France. The system described runs on a microcomputer and tackles multiple linear regression.
Google Scholar
|
|
Gale W A, 1986b. “REX Review” In: Artificial Intelligence and Statistics, Gale N A, (Ed.) Reading Massachusetts: Addison Wesley pp. 173–228. This was the last and longest of a series of papers on REX. The papers before this do not add much. It describes what REX looked like when it ran, gives an example of the report generated by REX, and describes the inference engine and knowledge representation in detail. It does not discuss the statistical strategy in detail.
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Lubinsky D and Pregibon D, “Data analysis as search”, Journal of Econometrics, 06, 1988. TESS, the system described, aims to model the results obtained by statisticians in analysing data, not the methods they use. The example studied is regression.
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Wolstenholme D and Neider J, 1986. “A front end for GLIM” In: Expert Systems in Statistics, Haux R, ed, Stuttgart and New York: Gustav Fischer. The paper describes early work on GLIMPSE, a front end for the popular statistical system GLIM. The book cited has about ten other papers, but they were written at a planning stage rather than at a stage with systems developed.
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Gale W A, 1986c. “Student phase 1—a report on work in progress” In: Artificial Intelligence and Statistics, Gale W A, (Ed.), Reading, Massachusetts: Addison Wesley (1986a) pp. 239–266. This described the first version of Student, implemented on a Symbolics Lisp Machine.
Google Scholar
|
|
Gale W A, 1987. “Student: a tool for constructing consultation systems in data analysis” In: Proceedings of the 46th Session of the International Statistics Institute, Tokyo. This described the second version of Student, implemented in the new S statistical language.
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|
Gale W A and Pregibon D, 1984. “Constructing an expert system for data analysis by working examples” In: COMPSTAT 1984: Proceedings in Computational Statistics, Havranck T, Sidak Z and Novak M, (Eds) Vienna: Physica-Verlag, pp. 227–236. This paper described the motivation and plans for building Student.
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|
Hand D J, 1987. “A statistical knowledge enhancement system” preprint, Institute of Psychiatry, London. KENS aids a statistician as an on-line reference book.
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|
Oldford W and Peters S, 1986b. “Data analysis networks in DINDE” Proceedings of the Statistical Computing Section, Arlington, Virginia: American Statistical Association, pp. 11–18. This paper describes DINDE's behavior and the knowledge representation behind it.
Google Scholar
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Becker R A and Chambers J M, 1984. S: an Interactive Environment for Data Analysis and Graphics, California: Wadsworth, Belmont. This is the manual for the use of (old) S, a statistical computing system produced by AT&T Bell Laboratories. It contains information on ordering S.
Google Scholar
|
|
Becker R A and Chambers J M, 1986. “Auditing of data analysis” In: Proceedings of Statistical Computation Section, Arlington, Virginia: American Statistical Association, pp. 11–18. The paper describes the use of the (new) S, or QPE, in a task of use to statisticians.
Google Scholar
|
|
Mallows C L, 1973. “Some comments on C sub P”, Technometrics15 pp. 661–667. This paper defined Cp.
Google Scholar
|
|
Tukey J W, 1983. “Another look at the future” Heiner Sachs, and Wilkinson , (Eds.) Computer Science and Statistics, Proceedings of the Fourteenth Symposium on the Interface, New York: Springer-Verlag pp. 1–8. This paper poses a dozen challenging tasks for statistical computing, some of which may be feasible with K.E methods.
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Tukey J W, 1986. “An alphabet for statisticians' expert systems” In: Artificial Intelligence and Statistics, Gale W A, (Ed.), Reading, Massachusetts: Addison Wesley, pp. 401–409. This paper appraises the strengths and weaknesses of the idea of applying KE techniques in statistics.
Google Scholar
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|
Velleman P F and Hoaglin D C, 1981. Applications, Basics and Computing for Exploratory Data Analysis, North Scituate, Massachusetts: Duxbury Press. This book provides practical computer programs and the basic ideas of exploratory data analysis.
Google Scholar
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