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

Content Provider	IEEE Xplore Digital Library
Author	Kiselev, I. Alhajj, R.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Calgary, Calgary, AB (Kiselev, I.; Alhajj, R.)
Abstract	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.
Starting Page	148
Ending Page	153
File Size	1433220
Page Count	6
File Format	PDF
ISBN	9780769534961
DOI	10.1109/WIIAT.2008.368
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	market-based dynamic distributed resource allocation Adaptive systems Multiagent systems Uncertainty Online unsupervised learning multi-agent system Extraterrestrial measurements anytime coalition formation Unsupervised learning Intelligent agent Learning systems Clustering algorithms Time factors Intelligent systems
Content Type	Text
Resource Type	Article

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