Off-line Learning of Coordination in Functionally Structured Agents for Distributed Data Processing

When we design multi-agent systems for realistic, worth-oriented environments, coordination problems they present involve intricate and sophisticated interplay between the domain and the various system components. Achieving effective coordination in such systems is a difficult problem for a number of reasons like local views of problem-solving task 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 an appropriate coordination algorithm based on the present problem solving situation in the domain of distributed data processing