A framework for knowledge acquisition through techniques of concept learning

An integrative framework is developed for describing concept learning techniques that makes it possible to evaluate their relevance to knowledge engineering. The framework provides a general basis for relating concept learning to knowledge acquisition and is a starting point for the development of formal design rules. First, concept learning is framed in the context of knowledge acquisition. Then the general forms of input and concept representation such as logic, functions and procedures are discussed. Next, methods of biasing the search for a suitable concept are described and illustrated including: background knowledge, conceptual bias, composition bias, and preference orderings. Finally, modes of teacher interaction are reviewed, including the nature of examples given and the method of presenting them. The framework is illustrated by applying it to the better-documented concept learning systems. >

[1]  W. R. Garner.  Concept Learning: An Information- Processing Problem , 1964 .

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

[3]  William A. Woods,et al.  Computational Linguistics Transition Network Grammars for Natural Language Analysis , 2022 .

[4]  John H. Andreae,et al.  A new mechanism for a brain , 1976 .

[5]  Douglas B. Lenat,et al.  Computers and Thought Lecture: The Ubiquity of Discovery , 1977, IJCAI.

[6]  B. Gaines.  SYSTEM IDENTIFICATION, APPROXIMATION AND COMPLEXITY , 1977 .

[7]  Peter Andreae,et al.  A teachable machine in the real world , 1978 .

[8]  Michael J. C. Gordon,et al.  The Denotational Description of Programming Languages , 1979, Springer New York.

[9]  J. Brooke.  Thinking With The Teachable Machine,By JOHN H. ANDREAE. (London:Academic Press, 1977.) [ Pp. XVii+.] £7·80. , 1980 .

[10]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .

[12]  Douglas B. Lenat,et al.  EURISKO: A Program That Learns New Heuristics and Domain Concepts , 1983, Artif. Intell..

[13]  Douglas B. Lenat,et al.  Why AM and EURISKO Appear to Work , 1984, Artif. Intell..

[14]  Ehud Shapiro,et al.  Algorithmic Program Debugging , 1983 .

[15]  Kurt VanLehn,et al.  Felicity conditions for human skill acquisition: validating an ai-based theory , 1983 .

[16]  David Hawkins,et al.  An Analysis of Expert Thinking , 1983, Int. J. Man Mach. Stud..

[17]  Ryszard S. Michalski,et al.  A Theory and Methodology of Inductive Learning , 1983, Artificial Intelligence.

[18]  H. Simon,et al.  Rediscovering Chemistry with the Bacon System , 1983 .

[19]  Carl H. Smith,et al.  Inductive Inference: Theory and Methods , 1983, CSUR.

[20]  Peter M. Andreae,et al.  Constraint Limited Generalization: Acquiring Procedures From Examples , 1984, AAAI.

[21]  H. C. Williams,et al.  Factoring on a Computer , 1984 .

[22]  G. Collins,et al.  Transcending inductive category formation in learning , 1986, Behavioral and Brain Sciences.

[23]  Ian H. Witten,et al.  PROGRAMMING COMPUTER CONTROLLED SYSTEMS BY NON-EXPERTS , 1987 .

[24]  Bruce A MacDonald.  Improved Robot Design , 1987 .

[25]  David Haussler.  Bias, Version Spaces and Valiant's Learning Framework , 1987 .

[26]  Robert E. Stepp.  Machine Learning from Structured Objects , 1987 .

[27]  Ian H. Witten,et al.  CONCEPT LEARNING: A PRACTICAL TOOL FOR KNOWLEDGE ACQUISITION? , 1987 .

[28]  Jadzia Cendrowska,et al.  PRISM: An Algorithm for Inducing Modular Rules , 1987, Int. J. Man Mach. Stud..

[29]  Jeffrey M. Bradshaw,et al.  Expertise Transfer and Complex Problems: Using AQUINAS as a Knowledge-Acquisition Workbench for Knowledge-Based Systems , 1987, Int. J. Man Mach. Stud..

[30]  Bruce A. MacDonald.  PROGRAMMING COMPUTER CONTROLLERS BY GIVING EXAMPLES , 1987 .

[31]  Chris Carter,et al.  Assessing Credit Card Applications Using Machine Learning , 1987, IEEE Expert.

[32]  M. Pazzani.  Inducing Causal and Social Theories: A Prerequisite for Explanation-based Learning , 1987 .

[33]  David Lorge Parnas,et al.  Why Engineers Should Not Use Artificial Intelligence , 1988 .

[34]  Brent J. Krawchuk,et al.  On Asking the Right Questions , 1988, ML.

[35]  Ian H. Witten,et al.  Using Concept Learning for Knowledge Acquisition , 1988, Int. J. Man Mach. Stud..

[36]  P. Laird.  Learning from Good and Bad Data , 1988 .

[37]  Paul D. Scott,et al.  A Formal Analysis of Machine Learning Systems for Knowledge Acquisition , 1988, Int. J. Man Mach. Stud..

[38]  Ian H. Witten,et al.  SPECIFYING PROCEDURES TO OFFICE SYSTEMS , 1988 .

[39]  John H. Andreae,et al.  EXPERT CONTROL FOR A ROBOT BODY , 1991 .