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On case-based learnability of languages

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Abstract

Case-based reasoning is deemed an important technology to alleviate the bottleneck of knowledge acquisition in Artificial Intelligence (AI). In case-based reasoning, knowledge is represented in the form of particular cases with an appropriate similarity measure rather than any form of rules. The case-based reasoning paradigm adopts the view that an Al system is dynamically changing during its life-cycle which immediately leads to learning considerations.

Within the present paper, we investigate the problem of case-based learning of indexable classes of formal languages. Prior to learning considerations, we study the problem of case-based representability and show that every indexable class is case-based representable with respect to a fixed similarity measure. Next, we investigate several models of case-based learning and systematically analyze their strengths as well as their limitations. Finally, the general approach to case-based learnability of indexable classes of formal languages is prototypically applied to so-called containmet decision lists, since they seem particularly tailored to case-based knowledge processing.

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Author information

Authors and Affiliations

  1. FB Informatik, Universität Kaiserslautern, PF 3049, 67653, Kaiserslautern

    Christoph Globig

  2. FB IMN, HTWK Leipzig, PF 30066, 04251, Leipzig, Germany

    Klaus P. Jantke & Steffen Lange

  3. Fujitsu Laboratories Ltd., Institute for Social Information Science, 140, Miyamoto, 410-03, Numazu, Shizuoka, Japan

    Yasubumi Sakakibara

Authors
  1. Christoph Globig
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  2. Klaus P. Jantke
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  3. Steffen Lange
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  4. Yasubumi Sakakibara
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Additional information

This work summarizes and extends results from the authors’ papers in theProceedings of the 4th International Workshop on Analogical and Inductive Inference andthe 5th International Workshop on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence 872, Springer-Verlag, Berlin Heidelberg, 1994, pp. 106–120 and pp. 532–546, respectively.

Christoph Globig: He has studied Computer Science at the University of Kaiserslautern and graduated in 1993 with a Master Thesis on a comparison of inductive and case-based learning. Since 1993, he is member of the research group of Prof. Richter at the University of Kaiserslautern. His main research interest is in Machine Learning especially the foundations of case-based reasoning and Machine Learning.

Klaus P. Jantke: He graduated from Humboldt University Berlin with a Master’s Thesis in 1975. He received his Ph.D. in Computer Science in 1979 and his Habilitation at Humboldt in 1984. He worked as the Head of a Research Laboratory in Theoretical Computer Science and as a Vice-Director of the Computing Center at Humboldt University. Since 1987, Dr. Jantke is full professor at Leipzig University of Technology. His main research interest is in algorithmic learning theory. Besides this, he contributes to case-based reasoning, where his special interest is in learning issues and in structural similarity, and to knowledge-based process supervision and control, especially to planning. Dr. Jantke is member of the ACM, the EATCS, and the GI.

Steffen Lange: He graduated from Humboldt University Berlin with a Master’s Thesis in 1984. He received his Ph.D. in Computer Science at Humboldt University Berlin in 1988. Since 1989, Dr. Lange works at Leipzig University of Technology (nowadays HTWK Leipzig) within several research projects on algorithmic learning theory and related topics. His main scientific interest is in algorithmic learning theory. Besides this, he is interested in case-based reasoning, where his work is concentrated on both knowledge representation issues and learning issues.

Yasubumi Sakakibara: He received the B. Sc., M. Sc., and Dr. Sc. degrees from the Department of Information Sciences, Tokyo Institute of Technology, in 1983, 1985, and 1991, respectively. He is currently an associate professor at the Department of Information Sciences, Tokyo Denki University. From 1985 to 1996, he was a researcher at the Institute for Social Information Science (ISIS), Fujitsu Laboratories Ltd.. His current interest includes computational learning theory, formal language theory, complexity theory, statistical theory, and their applications. He is a member of EATCS, IPSJ, JSAI, and JSSST.

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Globig, C., Jantke, K.P., Lange, S. et al. On case-based learnability of languages. New Gener Comput 15, 59–83 (1997). https://doi.org/10.1007/BF03037560

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  • DOI: https://doi.org/10.1007/BF03037560

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