On the management of distributed learning agents

Andreas L. Prodromidis

On the management of distributed learning agents
Prodromidis, Andreas L.
Technical reports
Computer Science
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Columbia University Computer Science Technical Reports
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This thesis research concentrates on the problem of managing a distributed collection of intelligent learning agents across large and distributed databases. The main challenge is to identify and address the issues related to the efficiency, scalability,adaptivity and compatibility of these agents and the design and implementation of a complete and coherent distributed meta-learning system for large scale data mining applications. The resulting system should be able to scale with many large databases and make effective use of the available system resources. Furthermore, it should be capable to adapt to changes in its computational environment and be flexible enough to circumvent variances in database schema definitions. In this thesis proposal we present the architecture of JAM(Java Agents for Meta-learning),a distributed data mining system, and we describe in detail several methods to cope with the issues of scalability, efficiency, adaptivity and compatibility. Through experiments, performed on actual credit card and other public domain data sets, we evaluate the effectiveness and performance of our approaches and we demonstrate their potential.
Computer science
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Suggested Citation:
Andreas L. Prodromidis, 1997, On the management of distributed learning agents, Columbia University Academic Commons, http://hdl.handle.net/10022/AC:P:29355.

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