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Achieving effective cooperation in a multi-agent system is a difficult problem for a number of reasons such as limited and possibly out-dated views of activities of other agents and uncertainty about the outcomes of interacting non-local tasks. In this paper, we present a learning system called COLLAGE, that endows the agents with the capability to learn how to choose the most appropriate coordination strategy from a set of available coordination strategies. COLLAGE relies on meta-level information about agents' problem solving situations to guide them towards a suitable choice for a coordination strategy. We present empirical results that strongly indicate the effectiveness of the learning algorithm.