The use of meta-level Information in learning situation-specific coordination

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 algorithm that endows agents with the capability to choose the appropriate coordination algorithm from a set of available coordination algorithms based on meta-level information about their problem solving situations. We present empirical results that strongly indicate the effectiveness of the learning algorithm.