Abstract
Finding the right data representation is essential for virtually every machine learning task. We discuss an extension of this representation problem. In the collaborative representation problem, the aim is to find for each learning agent in a multi-agent system an optimal data representation, such that the overall performance of the system is optimized, while not assuming that all agents learn the same underlying concept. Also, we analyze the problem of keeping the common terminology in which agents express their hypothesis as compact and comprehensible as possible by forcing them to use the same features, where this is possible. We analyze the complexity of this problem and show under which conditions an optimal solution can be found. We then propose a simple heuristic algorithm and show that this algorithm can efficiently be implemented in a multi-agent system. The approach is exemplified on the problem of collaborative media organization and evaluated on a several synthetic and real world datasets.
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Wurst, M. (2008). Multi-agent Learning by Distributed Feature Extraction. In: Tuyls, K., Nowe, A., Guessoum, Z., Kudenko, D. (eds) Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning. AAMAS ALAMAS ALAMAS 2005 2007 2006. Lecture Notes in Computer Science(), vol 4865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77949-0_17
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DOI: https://doi.org/10.1007/978-3-540-77949-0_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77947-6
Online ISBN: 978-3-540-77949-0
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