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The role of abduction in chance discovery

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Abstract

Recently, researches on discovery science and knowledge discovery have been carried out in various fields. Basically they are types of learning that learn tendencies from the sets of data of the same or similar categories. In this sense, discovery is to discover the tendencies. As a result, they cannot predict the events that are different from the trend. On the other hand, abduction is thought of as an explanatory reasoning. Indeed, abduction is a reasoning to generate hypotheses to explain an observation. However, the original meaning of abduction was to discover new things that cannot be known in a simple way. In this paper, abduction is defined using the original definition that discovers something that cannot be easily predicted. Then, this paper shows a role of abduction that can suggest or foresee the events that are different from the trend. In fact, Abductive Analogical Reasoning that can generate new hypotheses is adopted to solve the problem.

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References

  1. Abe, A., “Applications of Abduction,” inProc. of ECAI98 Workshop on Abduction and Induction in AI, pp. 12–19, 1998.

  2. Abe, A., “Two-sided Hypotheses Generation for Abductive Analogical Reasoning,” inProc. of ICTAI99, pp. 145–152, 1999.

  3. Abe, A., “The Relation between Abductive Hypotheses and Inductive Hypotheses,”Abduction and Induction (Flach, P. and Kakas, T., eds.), Kluwer, pp. 169–180, 2000.

  4. Abe, A., “Abductive Analogical Reasoning,”Systems and Computers in Japan, 31, 1, pp. 11–19, 2000.

    Article  Google Scholar 

  5. Gentner, D., “Structure-mapping: A Theoretical Framework for Analogy,”Cognitive Science, 7, 155–170, 1983.

    Article  Google Scholar 

  6. Kasahara, K. et al., “Viewpoint-based Measurement of Semantic Similarity between Words,” inProc. of 5th. International Workshop on Artificial Intelligence and Statistics (LNS 112), pp. 433–442, 1996.

  7. Matsumura, N., Ishizuka, M. and Ohsawa, Y., “Discovering Promising New Topics on the WWW,” inProc. of KES2000, pp. 804–807, 2000.

  8. Matsumura, N., Ohsawa, Y. and Ishizuka, M., “Future Directions of Communities on the Web,” inProc. of SCI2001, VIII, pp. 406–409, 2001.

    Google Scholar 

  9. McBurney, P. and Parsons, S., “Risk Agoras: Dialectical Argumentation for Scientific Reasoning,” inProc. of the 16th Conference on Uncertainty in Artificial Intelligence, 2000.

  10. McBurney, P. and Parsons, S., “Risk Agoras: Using Dialectical Argumentation to Debate Risk,”International Journal of Risk Management, 2000.

  11. Nara, Y. and Ohsawa, Y., “Tools for Shifting Human Context into Disasters,” inProc. of KES2000, pp. 655–658, 2000.

  12. Nara, Y. and Ohsawa, Y., “Decision Tree as Perception Model of Significant Rare Events,”JSAI SIG Notes, SIG-KBS-A002, pp. 1–6, 2000.

  13. Ohsawa, Y. and Yachida, M., “Discover Risky Active Faults by Indexing an Earthquake Sequence,” inProc. of DS’99(LNAI 1721), 1999.

  14. Ohsawa, Y., “Chance Discovery for Making Decision in Complex Real World,”New Generation Computings, 20, 2, pp. 143–163, 2002.

    Article  MATH  Google Scholar 

  15. Peirce, C. S., “Abduction and Induction,”Philosophical Writings of Peirce, 11, Dover, 1955.

  16. Poole, D., Goebel, R. and Aleliunas, R., “Theorist: A Logical Reasoning System for Defaults and Diagnosis,” The Knowledge Frontier: Essays in the Representation of Knowledge (Cercone, N. J. and McCalla, G., Eds.), Springer-Verlag, pp. 331–352, 1987.

  17. Reiter, R. and de Kleer, J., “Foundation of Assumption-based Truth Maintenance Systems: Preliminary Report,” inProc. of AAAI87, pp. 183–188, 1987.

  18. Suzuki, E., “Scheduled Discovery of Exception Rules,” inProc. of DS’99 (LNAI 1721), pp. 184–195, 1999.

  19. Takeuchi, K., “Where Probabilistic and Stochastic Society Goes?”revue de la pensée d’aujourd’hui, 28, 1, pp. 84–99, 2000 (in Japanese).

    Google Scholar 

  20. Tsumoto, S., “Chance Discovery and Detection of Outliers,”JSAI SIG Notes, SIG-FAI-A003, pp. 43–46, 2000 (in Japanese).

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Authors and Affiliations

  1. NTT MSC, No. 43000, Jalan APEC, 63000, Cyberjaya, Selangor Darul Ehsan, Malaysia

    Akinori Abe

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  1. Akinori Abe
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Correspondence to Akinori Abe.

Additional information

Akinori Abe, Ph.D.: He obtained his Doctor of Engineering (Ph.D) from the University of Tokyo in 1991, with a thesis entitledA Fast Hypothetical Reasoning System using Analogical Case. His main research interests are abduction (hypothetical reasoning), analogical reasoning, chance dicovery and language sense processing. He is a member of the Planning Committee of the New Generation Computing. He worked in NTT MSC (Malaysia) from 2000 to 2002. Currently, he works in ATR.

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Abe, A. The role of abduction in chance discovery. NGCO 21, 61–71 (2003). https://doi.org/10.1007/BF03042326

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  • DOI: https://doi.org/10.1007/BF03042326

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