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Discover unknown causes from inferred and visualized Co-occurring events

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Abstract

It is hard to have knowledge including all events which may have caused observed events. This makes it difficult to infer significant causes of observed events. However, unexpected relations detected between known events by a computer suggest unknown events to humans, being combined with the vast human knowledge acquired by rich experience. This paper presents a method to have a computer express “unknown” hidden causes, i.e. not included in the given knowledge. The inference method of the computer, for inferring known causes of observed time-series events, is Cost-based Cooperation of Multi-Abducers (CCMA) here aiming at detecting unexpectedly strong co-occurrences among known events. The detected relations are expressed to user, which makes significant unknown causal events easily understood. The empirical results encourages that the presented method helps in discovering significant unknown events.

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Author information

Authors and Affiliations

  1. Graduate School of Systems Management, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, 112-0012, Tokyo, Japan

    Yukio Ohsawa

  2. Graduate School of Engineering Science, Osaka University, 1-3 Toyonaka, 560-8531, Osaka, Japan

    Masahiko Yachida

Authors
  1. Yukio Ohsawa
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  2. Masahiko Yachida
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Additional information

Yukio Ohsawa, Ph.D.: He is an Associate Professor in the Graduate School of Systems Management, University of Tsukuba. He obtained his bachelors, masters, and Ph.D. degrees in Engineering from the University of Tokyo in 1990, 1992 and 1995 respectively. He was a research associate in Osaka University from 1995 to 1999. His research interests are in discovering signs of future events affecting human life, from data, based on his background of artificial intelligence. He received the Paper Award from the Japanese Society of Artificial Intelligence in 1999 and some awards for conference papers. He has served on program commitees of several conferences and workshops on AI and agents, currently chairing Multi-agent and Cooperative Computing workshops (MACC99).

Masahiko Yachida, Ph.D.: He is a professor at the Dept. of Systems Engineering of Osaka University since 1993. He obtained his B. E., M.Sc in electrical engineering and Ph.D. in control engineering from Osaka University in 1969, 1971, and 1976 respectively. He became a professor of the Dept. of Information and Computer Science of the same university in 1990, and moved to the current department as a professor. He was a research fellow at the Fachbereich Informatik, Hamburg University from 1981 to 1982, and a CDC professor at the Dept. of Computer Science, University of Minessota in 1983. He received several prizes including Ohkawa Publishing Prize, and is presently a Chairman of Technical Committee on Pattern Recognition & Media Understanding.

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Ohsawa, Y., Yachida, M. Discover unknown causes from inferred and visualized Co-occurring events. New Gener Comput 18, 75–86 (2000). https://doi.org/10.1007/BF03037570

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  • DOI: https://doi.org/10.1007/BF03037570

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