A Self-organizing Multi-agent System for Online Unsupervised Learning in Complex Dynamic Environments
نویسندگان
چکیده
The task of continuous online unsupervised learning of streaming data in complex dynamic environments under conditions of uncertainty is an NP-hard optimization problem for general metric spaces. This paper describes a computationally efficient 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. Experimental results demonstrate the strong performance of the implemented multi-agent learning system for continuous online optimization of both synthetic datasets and datasets from the RoboCup Soccer and Rescue domains.
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