Journal of Philosophical Logic 24 (5):525 - 548 (1995)

Authors
Bernhard Lauth
Ludwig Maximilians Universität, München
Abstract
Most standard results on structure identification in first order theories depend upon the correctness and completeness (in the limit) of the data, which are provided to the learner. These assumption are essential for the reliability of inductive methods and for their limiting success (convergence to the truth). The paper investigates inductive inference from (possibly) incorrect and incomplete data. It is shown that such methods can be reliable not in the sense of truth approximation, but in the sense that the methods converge to "empirically adequate" theories, i.e. theories, which are consistent with all data (past and future) and complete with respect to a given complexity class of L-sentences. Adequate theories of bounded complexity can be inferred uniformly and effectively by polynomial-time learning algorithms. Adequate theories of unbounded complexity can be inferred pointwise by less efficient methods
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DOI 10.1007/BF01052601
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References found in this work BETA

Objective Knowledge: An Evolutionary Approach.Karl Raimund Popper - 1972 - Oxford, England: Oxford, Clarendon Press.
Conjectures and Refutations.K. Popper - 1963 - Les Etudes Philosophiques 21 (3):431-434.
The Logic of Reliable Inquiry.Kevin T. Kelly - 1996 - Oxford, England: Oxford University Press USA.
Identification in the Limit of First Order Structures.Daniel Osherson & Scott Weinstein - 1986 - Journal of Philosophical Logic 15 (1):55 - 81.

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Citations of this work BETA

New Blades for Occam's Razor.Bernhard Lauth - 1997 - Erkenntnis 46 (2):241-267.

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