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  1. Are there really two types of learning?Yorick Wilks - 1986 - Behavioral and Brain Sciences 9 (4):671-671.
  • Protocol analysis as a task for artificial intelligence.D. A. Waterman & A. Newell - 1971 - Artificial Intelligence 2 (3-4):285-318.
  • The hard questions about noninductive learning remain unanswered.Eric Wanner - 1986 - Behavioral and Brain Sciences 9 (4):670-670.
  • Rejecting induction: Using occam's razor too soon.J. T. Tolliver - 1986 - Behavioral and Brain Sciences 9 (4):669-670.
  • The pragmatics of induction.Paul Thagard - 1986 - Behavioral and Brain Sciences 9 (4):668-669.
  • Salvaging parts of the “classical theory” of categorization.Dan Sperber - 1986 - Behavioral and Brain Sciences 9 (4):668-668.
  • Category differences/automaticity.Edward E. Smith - 1986 - Behavioral and Brain Sciences 9 (4):667-667.
  • Artificial intelligence: an empirical science.Herbert A. Simon - 1995 - Artificial Intelligence 77 (1):95-127.
  • Theory-laden concepts: Great, but what is the next step?Charles P. Shimp - 1986 - Behavioral and Brain Sciences 9 (4):666-667.
  • The learning of function and the function of learning.Roger C. Schank, Gregg C. Collins & Lawrence E. Hunter - 1986 - Behavioral and Brain Sciences 9 (4):672-686.
  • Transcending inductive category formation in learning.Roger C. Schank, Gregg C. Collins & Lawrence E. Hunter - 1986 - Behavioral and Brain Sciences 9 (4):639-651.
    The inductive category formation framework, an influential set of theories of learning in psychology and artificial intelligence, is deeply flawed. In this framework a set of necessary and sufficient features is taken to define a category. Such definitions are not functionally justified, are not used by people, and are not inducible by a learning system. Inductive theories depend on having access to all and only relevant features, which is not only impossible but begs a key question in learning. The crucial (...)
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  • Using empirical analysis to refine expert system knowledge bases.Peter Politakis & Sholom M. Weiss - 1984 - Artificial Intelligence 22 (1):23-48.
  • Approaches, assumptions, and goals in modeling cognitive behavior.Richard E. Pastore & David G. Payne - 1986 - Behavioral and Brain Sciences 9 (4):665-666.
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  • The psychology of category learning: Current status and future prospect.Gregory L. Murphy - 1986 - Behavioral and Brain Sciences 9 (4):664-665.
  • Generalization as search.Tom M. Mitchell - 1982 - Artificial Intelligence 18 (2):203-226.
  • Explanation-based learning:A problem solving perspective.Steven Minton, Jaime G. Carbonell, Craig A. Knoblock, Daniel R. Kuokka, Oren Etzioni & Yolanda Gil - 1989 - Artificial Intelligence 40 (1-3):63-118.
  • Of what use categories?Ruth Garrett Millikan - 1986 - Behavioral and Brain Sciences 9 (4):663-664.
  • A theory and methodology of inductive learning.Ryszard S. Michalski - 1983 - Artificial Intelligence 20 (2):111-161.
  • Extended Hierarchical Censored Production Rules (EHCPRs) System: An Approach Toward Generalized Knowledge Representation.N. K. Jain, K. K. Bharadwaj & Norian Marranghello - 1999 - Journal of Intelligent Systems 9 (3-4):259-295.
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  • When explanation is too hard (or understanding hijacking for novices).Michael Lebowitz - 1986 - Behavioral and Brain Sciences 9 (4):662-663.
  • Why a Diagram is (Sometimes) Worth Ten Thousand Words.Jill H. Larkin & Herbert A. Simon - 1987 - Cognitive Science 11 (1):65-100.
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  • New failures to learn.Barbara Landau - 1986 - Behavioral and Brain Sciences 9 (4):660-661.
  • Induction and explanation: Complementary models of learning.Pat Langley - 1986 - Behavioral and Brain Sciences 9 (4):661-662.
  • Induction and probability.Henry E. Kyburg - 1986 - Behavioral and Brain Sciences 9 (4):660-660.
  • Second-generation AI theories of learning.David Kirsh - 1986 - Behavioral and Brain Sciences 9 (4):658-659.
  • Clarity, generality, and efficiency in models of learning: Wringing the MOP.Kevin T. Kelly - 1986 - Behavioral and Brain Sciences 9 (4):657-658.
  • Learning production systems from examples.Charles L. Hedrick - 1976 - Artificial Intelligence 7 (1):21-49.
  • Transcending “transcending…”.Stephen Jośe Hanson - 1986 - Behavioral and Brain Sciences 9 (4):656-657.
  • Complementing explanation with induction.Clark Glymour - 1986 - Behavioral and Brain Sciences 9 (4):655-656.
  • Induction: Weak but essential.Thomas G. Dietterich - 1986 - Behavioral and Brain Sciences 9 (4):654-655.
  • Relevant features and statistical models of generalization.James E. Corter - 1986 - Behavioral and Brain Sciences 9 (4):653-654.
  • A new approach to the symbolic factorization of multivariate polynomials.Billy G. Claybrook - 1976 - Artificial Intelligence 7 (3):203-241.
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  • Toward a cognitive science of category learning.Robert L. Campbell & Wendy A. Kellogg - 1986 - Behavioral and Brain Sciences 9 (4):652-653.
  • An analytical comparison of some rule-learning programs.Alan Bundy, Bernard Silver & Dave Plummer - 1985 - Artificial Intelligence 27 (2):137-181.
  • Classifier systems and genetic algorithms.L. B. Booker, D. E. Goldberg & J. H. Holland - 1989 - Artificial Intelligence 40 (1-3):235-282.
  • The challenge of poker.Darse Billings, Aaron Davidson, Jonathan Schaeffer & Duane Szafron - 2002 - Artificial Intelligence 134 (1-2):201-240.
  • Are there static category representations in long-term memory?Lawrence W. Barsalou - 1986 - Behavioral and Brain Sciences 9 (4):651-652.
  • Category learning: Things aren't so black and white.John R. Anderson - 1986 - Behavioral and Brain Sciences 9 (4):651-651.
  • Prototypical knowledge for expert systems.Janice S. Aikins - 1983 - Artificial Intelligence 20 (2):163-210.