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Ensemble Case Based Learning for Multi-Agent Systems. Edition No. 1
VDM Publishing House, March 2008, Pages: 300
This book presents a framework for machine learning in a distributed
data scenario with decentralized decision making. We have based our
framework in Multi-Agent Systems (MAS), and in Case-Based Reasoning
(CBR). Moreover, we are interested in autonomous agents that
collaboratively work as ensembles. Specifically, we present the
Multi-Agent Case Based Reasoning (MAC) framework, a multi-agent
approach to CBR. Each individual agent in a MAC system is capable of
individually learn and solve problems using CBR with an individual
case base. Moreover, each case base is owned and managed by an
individual agent, and any information is disclosed or shared only if
the agent decides so. Thus, this framework preserves the privacy of
data, and the autonomy to disclose data. The focus of this book is
to develop strategies so that individual learning agents improve
their performance both individually and as an ensemble. The
techniques presented fall in between the fields of CBR and MAS, and
thus they aim at providing insights on how to successfully combine
both fields.
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