Algorithmic Learning Theory - ALT '92

1. Front Matter

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2. Discovery learning in intelligent tutoring systems

   • Setsuko Otsuki

   Pages 1-12

3. From inductive inference to algorithmic learning theory

   • Rolf Wiehagen

   Pages 13-24

4. A stochastic approach to genetic information processing

   • Akihiko Konagaya

   Pages 25-36

5. On learning systolic languages

   • Takashi Yokomori

   Pages 37-52

6. A note on the query complexity of learning DFA

   • José L. Balcázar, Josep Díaz, Ricard Gavaldà, Osamu Watanabe

   Pages 53-62

7. Polynomial-time MAT learning of multilinear logic programs

   • Kimihito Ito, Akihiro Yamamoto

   Pages 63-74

8. Iterative weighted least squares algorithms for neural networks classifiers

   • Takio Kurita

   Pages 75-86

9. Domains of attraction in autoassociative memory networks for character pattern recognition

   • Koichi Niijima

   Pages 87-98

10. Regularization learning of neural networks for generalization

    • Shotaro Akaho

    Pages 99-110

11. Competitive learning by entropy minimization

    • Ryotaro Kamimura

    Pages 111-122

12. Inductive inference with bounded mind changes

    • Yasuhito Mukouchi

    Pages 123-134

13. Efficient inductive inference of primitive Prologs from positive data

    • Hiroki Ishizaka, Hiroki Arimura, Takeshi Shinohara

    Pages 135-146

14. Monotonic language learning

    • Shyam Kapur

    Pages 147-158

15. Prudence in vacillatory language identification (Extended abstract)

    • Sanjay Jain, Arun Sharma

    Pages 159-168

16. Implementation of heuristic problem solving process including analogical reasoning

    • Kazuhiro Ueda, Saburo Nagano

    Pages 169-182

17. Planning with abstraction based on partial predicate mappings

    • Yoshiaki Okubo, Makoto Haraguchi

    Pages 183-194

18. Learning k-term monotone Boolean formulae

    • Yoshifumi Sakai, Akira Maruoka

    Pages 195-207

19. Some improved sample complexity bounds in the probabilistic PAC learning model

    • Jun-ichi Takeuchi

    Pages 208-219

20. An application of Bernstein polynomials in PAC model

    • Masahiro Matsuoka

    Pages 220-228

This volume contains the papers that were presented at the Third Workshop onAlgorithmic Learning Theory, held in Tokyo in October 1992. In addition to 3invited papers, the volume contains 19 papers accepted for presentation, selected from 29 submitted extended abstracts. The ALT workshops have been held annually since 1990 and are organized and sponsored by the Japanese Society for Artificial Intelligence. The main objective of these workshops is to provide an open forum for discussions and exchanges of ideasbetween researchers from various backgrounds in this emerging, interdisciplinary field of learning theory. The volume is organized into parts on learning via query, neural networks, inductive inference, analogical reasoning, and approximate learning.

• Artificial intelligence
• algorithm
• algorithms
• intelligence
• learning
• learning theory
• logical reasoning

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