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Titlebook: Adaptive Learning Agents; Second Workshop, ALA Matthew E. Taylor,Karl Tuyls Conference proceedings 2010 Springer-Verlag Berlin Heidelberg 2

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Replicator Dynamics for Multi-agent Learning: An Orthogonal Approach,978-3-658-40877-0
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Decentralized Learning in Wireless Sensor Networks,978-3-658-26733-9
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Coordinating Learning Agents for Multiple Resource Job Scheduling,978-3-531-19206-2
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https://doi.org/10.1007/978-94-011-3118-6driven by the replicator dynamics. Rather than letting the time dimension collapse, the set of initial policies is considered to be a particle cloud that approximates a distribution and we look at the evolution of this distribution over time. First, the methodology is described, then it is applied t
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The Electrical Installation Industry, random rewiring but with much less rewiring costs. We evaluate the effects of the number of trading partners on the utilities obtained by the agents. We hypothesize on the cause for the observed performance differences and verify that by showing that the observed performance differences with more r
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The Electrical Installation Industry,e a multiagent coordination approach to distributed, multi-resource job scheduling across heterogeneous servers. In this approach, agents at servers make local decisions to optimize an agent specific objective. The agent objectives though, are derived so that they are aligned with the overall effici
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0302-9743 al review round in the summer of 2009. We therefore wish to explicitly thank the members of the Program Committee for the quality and sincerity of their e?orts 978-3-642-11813-5978-3-642-11814-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
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