  Asymmetric distributed constraint optimization problems (ADCOPs) are an
emerging model for coordinating agents with personal preferences. However, the
existing inference-based complete algorithms which use local eliminations
cannot be applied to ADCOPs, as the parent agents are required to transfer
their private functions to their children. Rather than disclosing private
functions explicitly to facilitate local eliminations, we solve the problem by
enforcing delayed eliminations and propose AsymDPOP, the first inference-based
complete algorithm for ADCOPs. To solve the severe scalability problems
incurred by delayed eliminations, we propose to reduce the memory consumption
by propagating a set of smaller utility tables instead of a joint utility
table, and to reduce the computation efforts by sequential optimizations
instead of joint optimizations. The empirical evaluation indicates that
AsymDPOP significantly outperforms the state-of-the-arts, as well as the
vanilla DPOP with PEAV formulation.
