Analogical and Inductive Inference

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

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.

• Cognitive science
• algorithm
• algorithms
• artificial intelligence
• case-based reasoning
• intelligence
• learning
• logic
• logical reasoning
• programming

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