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  1. Robust logics.Leslie G. Valiant - 2000 - Artificial Intelligence 117 (2):231-253.
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  • Innateness and the brain.Steven R. Quartz - 2003 - Biology and Philosophy 18 (1):13-40.
    The philosophical innateness debate has long relied onpsychological evidence. For a century, however, a parallel debate hastaken place within neuroscience. In this paper, I consider theimplications of this neuroscience debate for the philosophicalinnateness debate. By combining the tools of theoretical neurobiologyand learning theory, I introduce the ``problem of development'' that alladaptive systems must solve, and suggest how responses to this problemcan demarcate a number of innateness proposals. From this perspective, Isuggest that the majority of natural systems are in fact innate. (...)
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  • Principles of metareasoning.Stuart Russell & Eric Wefald - 1991 - Artificial Intelligence 49 (1-3):361-395.
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  • Theory refinement combining analytical and empirical methods.Dirk Ourston & Raymond J. Mooney - 1994 - Artificial Intelligence 66 (2):273-309.
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  • Solving the multiple instance problem with axis-parallel rectangles.Thomas G. Dietterich, Richard H. Lathrop & Tomás Lozano-Pérez - 1997 - Artificial Intelligence 89 (1-2):31-71.
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  • Machine learning and the foundations of inductive inference.Francesco Bergadano - 1993 - Minds and Machines 3 (1):31-51.
    The problem of valid induction could be stated as follows: are we justified in accepting a given hypothesis on the basis of observations that frequently confirm it? The present paper argues that this question is relevant for the understanding of Machine Learning, but insufficient. Recent research in inductive reasoning has prompted another, more fundamental question: there is not just one given rule to be tested, there are a large number of possible rules, and many of these are somehow confirmed by (...)
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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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  • The complexity of theory revision.Russell Greiner - 1999 - Artificial Intelligence 107 (2):175-217.
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  • PALO: a probabilistic hill-climbing algorithm.Russell Greiner - 1996 - Artificial Intelligence 84 (1-2):177-208.
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  • Reasoning about model accuracy.Daniel S. Weld - 1992 - Artificial Intelligence 56 (2-3):255-300.
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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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  • Noise modelling and evaluating learning from examples.Ray J. Hickey - 1996 - Artificial Intelligence 82 (1-2):157-179.
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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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  • Embedding decision-analytic control in a learning architecture.Oren Etzioni - 1991 - Artificial Intelligence 49 (1-3):129-159.
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