ACM Computing Surveys (CSUR)
Dynamic Distributed Resource Allocation: A Distributed Constraint Satisfaction Approach
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Application of Double Clustering to Gene Expression Data for Class Prediction
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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In order to be effective in solving time-critical problems in complex dynamic environments with higher levels of uncertainty, an intelligent system must continuously adapt parameters of its learning system to variations in the incoming signals generated by the non-stationary environment in a real-time fashion. 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. Further research on extending the adaptive learning approach to support online semi-supervised classification by continuously deducing semantic-based classification rules from clustering results and performing automatic rule-based classification at run-time is outlined.