Abstract
In this paper an approach to the model-directed induction of inferential knowledge is discussed. In contrast to most existing programs that employ one single rule model, this approach is based on the heuristically-guided generation of several rule models. Furthermore, tentative ideas as how to achieve a fully operational system in the early stages of induction, are presented.
Preview
Unable to display preview. Download preview PDF.
Literatur
Carey, S.: “The Child as Word Learner”; in: M. Halle, J. Bresnan, G. Miller (eds.): Linguistic Theory and Psychological Reality, Cambridge, Mass. 1978
Dietterich, T.G./ London, B./ Clarkson, K./ Dromey, G.: “Learning and Inductive Inference”; Kapitel XIV, 3.Band von Cohen/Feigenbaum (eds.): The Handbook of Artificial Intelligence, Kaufmann, Los Altos, 1982
Dietterich, T.G./ Michalski, R.S.: “Discovering Patterns in Sequences of Events”, Artificial Intelligence 25, S. 187–232, 1985
Emde, W.: “Kontrainduktives Lernen von Konzepten aus Fakten”; In: B.Neumann (ed.): GWAI-83, 7th German Workshop on Artificial Intelligence; Springer, Berlin, 1983
Emde, W.: “Inkrementelles Lernen mit heuristisch generierten Modellen”; KIT-Report 22, 1984
Emde, W./ Hebel, Ch./ Rollinger, C.-R.: “The Discovery of the Equator (or Concept Driven Learning)”; In: Proc. IJCAI-83, Karlsruhe, 1983
Hayes-Roth, F.: “Using Proofs and Refutations to Learn from Experience”; In: Michalski/Carbonell/Mitchell (eds.): Machine Learning; Tioga Press, Palo Alto, 1983
Inhelder, B./ Piaget, J.: “The Law of Floating Bodies and the Elimination of Contradictions”; In: Inhelder/Piaget:The Growth of Logical Thinking; Routledge & Kegan Paul Ltd., London, 1968
Lakatos, I.: “Beweise und Widerlegungen”; Vieweg, 1979
Langley, P./ Zytkow, J./ Simon, H.A./ Bradshaw, G.L.: “Mechanism for Qualitative and Quantitative Discovery”; In: Proc. International Maschine Learning Workshop, Monticello, Illinois, 1983
Mitchell, T.M.: “Generalisation as Search”; Artificial Intelligence 18, S. 203–226, 1982
Simon, H.A.: “Why should Machines learn?”; In: Michalski/Carbonell/Mitchell (eds.): Machine Learning; Tioga Press, Palo Alto, 1983
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1985 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Emde, W. (1985). Maschinelles Lernen mit heuristisch generierten Modellen. In: Trost, H., Retti, J. (eds) Österreichische Artificial Intelligence-Tagung. Informatik-Fachberichte, vol 106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-46552-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-46552-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-15695-6
Online ISBN: 978-3-642-46552-9
eBook Packages: Springer Book Archive