Abstract
We have been developing a methodology and system for autonomous knowledge discovery and data mining from global information sources. The key issue is how to increase both autonomy and versatility of our discovery system. Our methodology is to create an organized society of autonomous knowledge discovery agents. This means (1) to develop many kinds of knowledge discovery and data mining agents (KDD agents in short) for different objects; (2) to use the KDD agents in multiple learning phases in a distributed cooperative mode; (3) to manage the society of the KDD agents by multiple meta-control levels. Based on this methodology, a multi-strategy and cooperative discovery system, which can be imagined as a softbot and is named GLS (Global Learning Scheme), has being developing by us. This paper briefly describes our methodology and the framework of our GLS system.
Preview
Unable to display preview. Download preview PDF.
References
Chow, G.C. 1960. Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3).
Durfee,E.H. and Lesser,V.R. 1989. Negotiating Task Decomposition and Allocation using Partial Global Planning. Distributed Artificial Intelligence Vol.2.
Fayyad,U.M., Piatetsky-Shapiro,G et al (eds.) 1996. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press.
Langley, P. & Zytkow, J.M. 1989. Data-Driven Approaches to Empirical Discovery. Artificial Intelligence, 40(1–3):283–312.
Liu, C. and Conradi, R. 1993. Automatic Replanning of Task Networks for Process Evolution in EPOS. Proc. the 4th European Software Engineering Conference (ESEC'93), LNCS 717, Springer Verlag, pp.437–450.
Matheus, C.J., Chan, P.K. & Piatetsky-Shapiro, G. 1993. Systems for Knowledge Discovery in Databases. IEEE Trans. Knowl. Data Eng., 5(6):904–913.
Michalski, R.S. et al. 1992. Mining for Knowledge in Databases: The INLEN Architecture, Initial Implementation and First Results. J. of Intell. Infor. Sys., KAP, 1(1):85–113.
Minsky,M. 1986 The Society of Mind, Simon and Schuster, New York.
T.M. Mitchell. Generalization as Search. Artificial Intelligence, Vol.18 (1982) 203–226.
Ohsuga, S. 1970. On the Value of Information and Decision Making. Information Processing in Japan, Vol.10, pp.97–108.
Ohsuga, S. & Yamauchi, H. 1985. Multi-Layer Logic — A Predicate Logic Including Data Structure as Knowledge Representation Language. New Generation Computing, 3(4):403–439.
Ohsuga, S. 1990. Framework of Knowledge Based Systems. Knowl. Based Sys., 3(4):204–214.
Ohsuga, S. 1995. A Way of Designing Knowledge Based Systems. Knowledge Based Systems, 8(4):211–222.
Ohsuga, S. Symbol Processing by Non-Symbol Processor. Proc. 4th Pacific Rim Int. Conf. on Artificial Intelligence (PRICAI'96) (1996).
Piatetsky-Shapiro, G. & Frawley, W.J. (eds.). 1991. Knowledge Discovery in Databases. AAAI/MIT Press.
Simon, H.A. & Ando, A. 1961. Aggregation of Variables in Dynamic Systems. Econometrica, 29:111–138.
Zhang,X. 1996. Co-scheduling Parallel Workloads across Networks of Workstations, invited talk at Yamaguchi Univ. Japan, June 1996.
Zhong, N. & Ohsuga, S. 1992. GLS — A Methodology for Discovering Knowledge from Databases. Proc. 13th Int. CODATA Conf. entitled “New Data Challenges in Our Information Age”, A20–A30.
Zhong, N. & Ohsuga, S. 1993. HML — An Approach for Managing/Refining Knowledge Discovered from Databases. Proc. 5th IEEE Int. Conf. on Tools with Artif. Intell. (TAI'93), 418–426.
Zhong, N. & Ohsuga, S. 1994a. Discovering Concept Clusters by Decomposing Databases. Data & Knowl. Eng., Elsevier Science Publishers, 12(2):223–244.
Zhong, N. & Ohsuga, S. 1994b. The GLS Discovery System: Its Goal, Architecture and Current Results. Proc. 8th Inter. Symp. on Methodologies for Intell. Sys. (ISMIS'94). LNAI 869, Springer, 233–244.
Zhong, N. & Ohsuga, S. 1994c. IIBR — A System for Managing/Refining Structural Characteristics Discovered from Databases. Proc. 6th IEEE Int. Conf. on Tools with Artif. Intell. (TAI'94), 468–475.
Zhong, N. & Ohsuga, S. 1995a. KOSI — An Integrated System for Discovering Functional Relations from Databases. J. of Intell. Infor. Sys., KAP, 5(1):20–50.
Zhong,N. and Ohsuga,S. 1995b. “Toward A Multi-Strategy and Cooperative Discovery System”, Proc First Inter. Conf. on Knowledge Discovery and Data Mining (KDD-95), AAAI Press, 337–342.
Zhong,N. and Ohsuga,S. 1996a. “System for Managing and Refining Structural Characteristics Discovered from Databases”, Knowledge Based Systems, Elsevier Science Publishers, 9(4):267–279.
Zhong,N. and Ohsuga,S. 1996b. “A Multi-Step Process for Discovering, Managing and Refining Strong Functional Relations Hidden in Databases”, Proc. 9th Inter. Symp. on Methodologies for Intell. Sys. (ISMIS'96). LNAI 1079, Springer, 501–510.
Zhong,N. and Ohsuga,S. 1996c. “A Hierarchical Model Learning Approach for Refining and Managing Concept Clusters Discovered from Databases”, Data & Knowl. Eng., Elsevier Science Publishers, 20(2): 227–252.
Zhong,N. and Ohsuga,S. 1996d. “Using Generalizations Distribution Tables as a Hypothesis Search Space for Generalization”, Proc. 4th Int. W. on Rough Sets, Fuzzy Sets, and Machine Discovery (RSFD'96) (in press).
Zhong,N. and Ohsuga,S. 1996e. “Representing Generalizations Distribution Tables by Connectionist Networks for Evolutionary Rule Discovery”, Proc. 1996 Asian Fuzzy Systems Symposium edited in the invited session on Rough Sets and Data Mining (in press).
Zytkow, J.M. 1993. Introduction: Cognitive Autonomy in Machine Discovery. Machine Learning, KAP, 12(1–3):7–16.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhong, N., Kakemoto, Y., Ohsuga, S. (1997). An organized society of autonomous knowledge discovery agents. In: Kandzia, P., Klusch, M. (eds) Cooperative Information Agents. CIA 1997. Lecture Notes in Computer Science, vol 1202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62591-7_33
Download citation
DOI: https://doi.org/10.1007/3-540-62591-7_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-62591-9
Online ISBN: 978-3-540-68321-6
eBook Packages: Springer Book Archive