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Communication management using abstraction in distributed bayesian networks

In complex distributed applications, such as distributed interpretation, a problem is often decomposed into a set of subproblems and each subproblem is distributed to an agent who will be responsible for solving it. The existence of interactions between subproblems means that the agents cannot simply solve the subproblems individually and then combine local solutions together. In such systems, the amount of communication among agents may be very significant in order to guarantee global optimality or even global consistency. Thus, "satisficing" approaches have been developed that trade off optimality for reduced communication [2]. An important characterization of such distributed protocols is how much communication is required and the likelihood that the solution will be the same as that generated by an optimal centralized algorithm which uses all available information.