SIGART special issue on machine learning

Current research on Machine Learning encompasses a diverse set of approaches, and of opinions regarding where the important issues lie. The significant increase of interest and research activity in Machine Learning over the past few years has led us to organize this special issue of SIGART, whose purpose is to provide a snapshot of current research in this field. This issue contains a set of summaries of ongoing research, solicited from the community at large, and received from thirty-five research groups from around the world. A substantial bibliography on Machine Learning is also included.

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[2]  Robert A. Kowalski,et al.  Algorithm = logic + control , 1979, CACM.

[3]  Ryszard S. Michalski,et al.  Pattern Recognition as Rule-Guided Inductive Inference , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  DANA ANGLUIN,et al.  On the Complexity of Minimum Inference of Regular Sets , 1978, Inf. Control..

[5]  Dana Angluin,et al.  Finding Patterns Common to a Set of Strings , 1980, J. Comput. Syst. Sci..

[6]  Tom M. Mitchell,et al.  A Model for Learning Systems , 1977, IJCAI.

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[8]  Kurt VanLehn,et al.  Repair Theory: A Generative Theory of Bugs in Procedural Skills , 1980, Cogn. Sci..

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[10]  Chitoor Srinivasan.  The META Description System: A System to Generate Intelligent Information Systems. Part 1. The Model Space , 1977 .

[11]  Raymond Aubin,et al.  Strategies for Mechanizing Structural Induction , 1977, IJCAI.

[12]  H A Simon,et al.  The theory of learning by doing. , 1979, Psychological review.

[13]  Ryszard S. Michalski,et al.  Variable-Valued Logic and Its Applications to Pattern Recognition and Machine Learning , 1975 .

[14]  Tom Michael Mitchell.  Version spaces: an approach to concept learning. , 1979 .

[15]  Chitoor V. Srinivasan,et al.  The Architecture of Coherent Information System: A General Problem Solving System , 1973, IEEE Transactions on Computers.

[16]  Ryszard S. Michalski,et al.  Selection of Most Representative Training Examples and Incremental Generation of VL1 Hypotheses: The Underlying Methodology and the Description of Programs ESEL and AQ11 , 1978 .

[17]  Derek H. Sleeman,et al.  Modelling Student's Problem Solving , 1981, Artif. Intell..

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[20]  Michael D. Rychener,et al.  A Semantic Network of Production Rules in a System for Describing Computer Structures , 1979, IJCAI.

[21]  James Burton Larson,et al.  Inductive inference in the variable valued predicate logic system vl21: methodology and computer implementation. , 1977 .

[22]  Yuichiro Anzai.  How to Learn Strategies: Processes and Representation of Strategy Acquisition , 1978, AISB/GI.

[23]  John R. Anderson,et al.  A Theory of the Acquisition of Cognitive Skills. , 1978 .

[24]  Gordon S. Novak,et al.  Research on Expert Problem Solving in Physics , 1980, AAAI.

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[26]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[27]  Ryszard S. Michalski,et al.  Inductive Learning as Rule-Guided Generalization and Conceptual Simplification of Symbolic Descriptions: Unifying principles and a methodology , 1980 .

[28]  John Seely Brown,et al.  Towards a Generative Theory of “Bugs” , 2020, Addition and Subtraction.

[29]  Sandra G. Harding.  Can theories be refuted? : essays on the Duhem-Quine thesis , 1976 .

[30]  David M. Sandford.  Using Sophisticated Models in Resolution Theorem Proving , 1980, Lecture Notes in Computer Science.

[31]  Lotfi A. Zadeh,et al.  PRUF—a meaning representation language for natural languages , 1978 .

[32]  John R. Anderson.  A General Learning Theory and Its Application to the Acquisition of Proof Skills in Geometry. , 1980 .

[33]  John Seely Brown,et al.  Diagnostic Models for Procedural Bugs in Basic Mathematical Skills , 1978, Cogn. Sci..

[34]  P. D. Summers,et al.  Program construction from examples. , 1975 .

[35]  Gordon S. Novak Jr..  Computer Understanding Of Physics Problems Stated In Natural Language , 1979, ACL Microfiche Series 1-83, Including Computational Linguistics.

[36]  Saul Amarel,et al.  On representations of problems of reasoning about actions , 1968 .

[37]  Gordon S. Novak.  Representations of Knowledge in a Program for Solving Physics Problems , 1977, IJCAI.

[38]  Michael D. Rychener.  Approaches to Knowledge Acquisition: The Instructable Production System Project , 1980, AAAI.

[39]  John R. Anderson,et al.  A General Learning Theory and its Application to Schema Abstraction1 , 1979 .

[40]  Philip Klahr,et al.  Design issues for exemplary programming , 1980 .