Inference-based complete algorithms for asymmetric distributed constraint optimization problems

Asymmetric distributed constraint optimization problems (ADCOPs) are an important framework for multiagent coordination and optimization, where each agent has its personal preferences. However, the existing inference-based complete algorithms that use local eliminations cannot be applied to ADCOPs, as the (pseudo) parents are required to transfer their private functions to their (pseudo) children to perform the local eliminations optimally. Rather than disclosing private functions explicitly to facilitate local eliminations, we solve the problem by enforcing delayed eliminations and propose the first inference-based complete algorithm for ADCOPs, named AsymDPOP. 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 the computation efforts by sequential eliminations instead of joint eliminations. To ensure the proposed algorithms can scale up to large-scale problems under the limited memory, we combine them with the memory-bounded inference by iteratively propagating the memory-bounded utility tables with the instantiation of cycle-cut (CC) nodes, where we propose to reduce the redundancy in bounded utility iterative propagation by enumerating CC nodes in different branches independently and propagating the utility tables within the memory limit only once. The empirical evaluation indicates that the proposed methods significantly outperform the state-of-the-art as well as the vanilla DPOP with PEAV formulation.