A Framework for Agent-based Distributed Machine Learning and Data Mining

Jan Tozicka, Michael Rovatsos, Michal Pechoucek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes among agents and (ii) online reasoning about learning success and learning progress by learning agents. We present an abstract architecture that enables agents to exchange models of their local learning processes and introduces a number of different methods for integrating these processes. This allows us to apply existing agent interaction mechanisms to distributed machine learning tasks, thus leveraging the powerful coordination methods available in agent-based computing, and enables agents to engage in meta-reasoning about their own learning decisions. We apply this architecture to a real-world distributed clustering application to illustrate how the conceptual framework can be used in practical systems in which different learners may be using different datasets, hypotheses and learning algorithms. We report on experimental results obtained using this system, review related work on the subject, and discuss potential future extensions to the framework.
Original languageEnglish
Title of host publicationAAMAS '07 Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Place of PublicationNew York, NY, USA
PublisherACM
Pages678-685
Number of pages8
ISBN (Print)978-81-904262-7-5
DOIs
Publication statusPublished - 2007

Keywords

  • distributed machine learning, frameworks and architectures, multiagent learning, unsupervised clustering

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