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An Adaptive Multi-agent System for Continuous Learning of Streaming Data

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2 Author(s)
Kiselev, I. ; Dept. of Comput. Sci., Univ. of Calgary, Calgary, AB ; Alhajj, R.

The task of continuous online unsupervised learning of streaming data in complex dynamic environments under conditions of uncertainty requires the maximizing (or minimizing) of a certain similarity-based objective function defining an optimal segmentation of the input data set into clusters, which is an NP-hard optimization problem in a general metric space and is computationally intractable for real-world problems of practical interest. This paper describes the developed adaptive multi-agent approach to continuous online clustering of streaming data, which is originally sensitive to environmental variations and provides a fast dynamic response with event-driven incremental improvement of optimization results, trading-off operating time and result quality. Our two main contributions include a computationally efficient market-based algorithm of continuous agglomerative hierarchical clustering of streaming data and a knowledge-based self-organizing multi-agent system for implementing it. Experimental results demonstrate the strong performance of the implemented multi-agent learning system for continuous online clustering of both synthetic datasets and datasets from the RoboCup Soccer and Rescue domains.

Published in:

Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on  (Volume:2 )

Date of Conference:

9-12 Dec. 2008

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