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Analogical and Inductive Inference

International Workshop AII '92, Dagstuhl Castle, Germany, October 5-9, 1992. Proceedings

  • Conference proceedings
  • © 1992

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Editors:
  1. Klaus P. Jantke
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 642)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: AII 1992.

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Table of contents (21 papers)

  1. Front Matter

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  2. Representing the spatial/kinematic domain and lattice computers

    • John Case, Dayanand S. Rajan, Anil M. Shende
    Pages 1-25

  3. A solution of the credit assignment problem in the case of learning rectangles

    • Zhixiang Chen, Wolfgang Maass
    Pages 26-34

  4. Learning decision strategies with genetic algorithms

    • John J. Grefenstette
    Pages 35-50

  5. Background knowledge and declarative bias in inductive concept learning

    • Nada Lavrač, Sašo Džeroski
    Pages 51-71

  6. Too much information can be too much for learning efficiently

    • Rolf Wiehagen, Thomas Zeugmann
    Pages 72-86

  7. Some experiments with a learning procedure

    • Kurt Ammon
    Pages 87-98

  8. Unions of identifiable classes of total recursive functions

    • Kalvis Apsītis, Rūsinš Freivalds, Mārtinš Krikis, Raimonds Simanovskis, Juris Smotrovs
    Pages 99-107

  9. Learning from multiple sources of inaccurate data

    • Ganesh Baliga, Sanjay Jain, Arun Sharma
    Pages 108-128

  10. Strong separation of learning classes

    • John Case, Keh-Jiann Chen, Sanjay Jain
    Pages 129-139

  11. Desiderata for generalization-to-N algorithms

    • William W. Cohen
    Pages 140-150

  12. The power of probabilism in Popperian FINite learning

    • Robert Daley, Bala Kalyanasundaram, Mahe Velauthapillai
    Pages 151-169

  13. An analysis of various forms of ‘jumping to conclusions’

    • Peter A. Flach
    Pages 170-186

  14. An inductive inference approach to classification

    • Rusins Freivalds, Achim G. Hoffmann
    Pages 187-196

  15. Asking questions versus verifiability

    • William I. Gasarch, Mahendran Velauthapillai
    Pages 197-213

  16. Predictive analogy and cognition

    • Bipin Indurkhya
    Pages 214-231

  17. Learning a class of regular expressions via restricted subset queries

    • Efim Kinber
    Pages 232-243

  18. A unifying approach to monotonic language learning on informant

    • Steffen Lange, Thomas Zeugmann
    Pages 244-259

  19. Characterization of finite identification

    • Yasuhito Mukouchi
    Pages 260-267

  20. A model of the ‘redescription’ process in the context of geometric proportional analogy problems

    • Scott O'Hara
    Pages 268-293
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  1. Analogical and Inductive Inference

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About this book

This volume contains the text of the five invited papers and 16 selected contributions presented at the third International Workshop on Analogical and Inductive Inference, AII `92, held in Dagstuhl Castle, Germany, October 5-9, 1992. Like the two previous events, AII '92 was intended to bring together representatives from several research communities, in particular, from theoretical computer science, artificial intelligence, and from cognitive sciences. The papers contained in this volume constitute a state-of-the-art report on formal approaches to algorithmic learning, particularly emphasizing aspects of analogical reasoning and inductive inference. Both these areas are currently attracting strong interest: analogical reasoning plays a crucial role in the booming field of case-based reasoning, and, in the fieldof inductive logic programming, there have recently been developed a number of new techniques for inductive inference.

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