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  1. Quantifying inductive bias: AI learning algorithms and Valiant's learning framework.David Haussler - 1988 - Artificial Intelligence 36 (2):177-221.
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  • Word learning as Bayesian inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
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  • Computing programs for generalized planning using a classical planner.Javier Segovia-Aguas, Sergio Jiménez & Anders Jonsson - 2019 - Artificial Intelligence 272 (C):52-85.
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  • Machine learning: An artificial intelligence approach.Mark J. Stefik - 1985 - Artificial Intelligence 25 (2):236-238.
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  • Creativity and learning in a case-based explainer.Roger C. Schank & David B. Leake - 1989 - Artificial Intelligence 40 (1-3):353-385.
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  • Learning Boolean concepts in the presence of many irrelevant features.Hussein Almuallim & Thomas G. Dietterich - 1994 - Artificial Intelligence 69 (1-2):279-305.
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  • Selection of relevant features and examples in machine learning.Avrim L. Blum & Pat Langley - 1997 - Artificial Intelligence 97 (1-2):245-271.
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  • Safe takeoffs—soft landings.D. Medin - 1990 - Cognitive Science 14 (1):169-178.
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  • From implicit skills to explicit knowledge: a bottom‐up model of skill learning.Edward Merrillb & Todd Petersonb - 2001 - Cognitive Science 25 (2):203-244.
    This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...)
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  • Learning one subprocedure per lesson.Kurt VanLehn - 1987 - Artificial Intelligence 31 (1):1-40.
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  • Computational approaches to analogical reasoning.Rogers P. Hall - 1989 - Artificial Intelligence 39 (1):39-120.
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  • Iterative versionspaces.Gunther Sablon, Luc De Raedt & Maurice Bruynooghe - 1994 - Artificial Intelligence 69 (1-2):393-409.
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  • From statistical relational to neurosymbolic artificial intelligence: A survey.Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve & Luc De Raedt - 2024 - Artificial Intelligence 328 (C):104062.
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  • Law, learning and representation.Kevin D. Ashley & Edwina L. Rissland - 2003 - Artificial Intelligence 150 (1-2):17-58.
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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.
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  • Learning concepts by arranging appropriate training order.Yao-Tung Hsu, Tzung-Pei Hong & Shian-Shyong Tseng - 2001 - Minds and Machines 11 (3):399-415.
    Machine learning has been proven useful for solving the bottlenecks in building expert systems. Noise in the training instances will, however, confuse a learning mechanism. Two main steps are adopted here to solve this problem. The first step is to appropriately arrange the training order of the instances. It is well known from Psychology that different orders of presentation of the same set of training instances to a human may cause different learning results. This idea is used here for machine (...)
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  • Models of incremental concept formation.John H. Gennari, Pat Langley & Doug Fisher - 1989 - Artificial Intelligence 40 (1-3):11-61.
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  • Categorizing Numeric Information for Generalization.Michael Lebowitz - 1985 - Cognitive Science 9 (3):285-308.
    Learning programs that generalize from real‐world examples will have to deal with many different kinds of data. Continuous numeric data can cause problems for algorithms that search for examples with identical property values. These problems can be surmounted by categorizing the numeric data. However, this process has problems of its own. In this paper, we look at the need for categorizing numeric data and several methods for doing so. We concentrate on the use of generalization‐based memory, a memory organization where (...)
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  • Logical settings for concept-learning.Luc De Raedt - 1997 - Artificial Intelligence 95 (1):187-201.
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  • A theory and methodology of inductive learning.Ryszard S. Michalski - 1983 - Artificial Intelligence 20 (2):111-161.
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  • Inductive learning by machines.Stuart Russell - 1991 - Philosophical Studies 64 (October):37-64.
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  • Automatic programming of behavior-based robots using reinforcement learning.Sridhar Mahadevan & Jonathan Connell - 1992 - Artificial Intelligence 55 (2-3):311-365.
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  • Grammatically biased learning: Learning logic programs using an explicit antecedent description language.William W. Cohen - 1994 - Artificial Intelligence 68 (2):303-366.
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  • (2 other versions)European Summer Meeting of the Association for Symbolic Logic, , Granada, Spain, 1987.H. -D. Ebbinghaus, J. Fernández-Prida, M. Garrido, D. Lascar & M. Rodriguez Artalejo - 1989 - Journal of Symbolic Logic 54 (2):647-672.
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  • On the Interaction of Theory and Data in Concept Learning.Edward J. Wisniewski & Douglas L. Medin - 1994 - Cognitive Science 18 (2):221-281.
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  • Automatic knowledge base refinement for classification systems.Allen Ginsberg, Sholom M. Weiss & Peter Politakis - 1988 - Artificial Intelligence 35 (2):197-226.
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  • A preliminary analysis of the Soar architecture as a basis for general intelligence.Paul S. Rosenbloom, John E. Laird, Allen Newell & Robert McCarl - 1991 - Artificial Intelligence 47 (1-3):289-325.
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  • Model-based reasoning about learner behaviour.Kees de Koning, Bert Bredeweg, Joost Breuker & Bob Wielinga - 2000 - Artificial Intelligence 117 (2):173-229.
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  • Induction and the discovery of the causes of scurvy: a computational reconstruction.Vincent Corruble & Jean-Gabriel Ganascia - 1997 - Artificial Intelligence 91 (2):205-223.
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  • Integrating representation learning and skill learning in a human-like intelligent agent.Nan Li, Noboru Matsuda, William W. Cohen & Kenneth R. Koedinger - 2015 - Artificial Intelligence 219 (C):67-91.
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  • Learning, action, and consciousness: A hybrid approach toward modeling consciousness.Ron Sun - 1997 - Neural Networks 10:1317-33.
    _role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approxi-_ _mate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety_ _of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning_ _and their respective products. The distinctions are captured in a two-level action-based model C_larion_. Some funda-_ _mental theoretical issues are also clari?ed with the help of the model. Comparisons with (...)
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  • Version spaces and the consistency problem.Haym Hirsh, Nina Mishra & Leonard Pitt - 2004 - Artificial Intelligence 156 (2):115-138.
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  • Exploiting the deep structure of constraint problems.Colin P. Williams & Tad Hogg - 1994 - Artificial Intelligence 70 (1-2):73-117.
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  • Schema induction for logic program synthesis.Nancy Lynn Tinkham - 1998 - Artificial Intelligence 98 (1-2):1-47.
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  • Learning structures of visual patterns from single instances.Yoshinori Suganuma - 1991 - Artificial Intelligence 50 (1):1-36.
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  • A Computational Theory of Learning Causal Relationships.Michael Pazzani - 1991 - Cognitive Science 15 (3):401-424.
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  • The description identification problem.Chris Mellish - 1991 - Artificial Intelligence 52 (2):151-167.
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  • Defining operationality for explanation-based learning.Richard M. Keller - 1988 - Artificial Intelligence 35 (2):227-241.
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  • Simulation Models of the Influence of Learning Mode and Training Variance on Category Learning.Renée Elio & Kui Lin - 1994 - Cognitive Science 18 (2):185-219.
    This article uses simulation as an empirical method for identifying process models of strategy effects in a category-learning task. A general set of learning assumptions defined a symbolic learning framework in which alternative simulation models were defined and tested. The goal was to identify process models that could account for previously reported data on the interaction between how a learner encounters category variance across a series of training samples and whether the task instructions suggested an active, hypothesis-testing approach, or a (...)
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  • Permissive planning: extending classical planning to uncertain task domains.Gerald F. DeJong & Scott W. Bennett - 1997 - Artificial Intelligence 89 (1-2):173-217.
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  • Generalized subsumption and its applications to induction and redundancy.Wray Buntine - 1988 - Artificial Intelligence 36 (2):149-176.
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  • Constraint acquisition.Christian Bessiere, Frédéric Koriche, Nadjib Lazaar & Barry O'Sullivan - 2017 - Artificial Intelligence 244 (C):315-342.
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  • Logical approaches to machine learning-an overview.P. Flach - 1992 - Think (misc) 1 (2):25-36.
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