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Asymmetric Distributed Constraint Optimization Problems (ADCOPs) have emergedas an important formalism in multi-agent community due to their ability tocapture personal preferences. However, the existing search-based completealgorithms for ADCOPs can only use local knowledge to compute lower bounds,which leads to inefficient pruning and prohibits them from solving large scaleproblems. On the other hand, inference-based complete algorithms (e.g., DPOP)for Distributed Constraint Optimization Problems (DCOPs) require only a linearnumber of messages, but they cannot be directly applied into ADCOPs due to aprivacy concern. Therefore, in the paper, we consider the possibility ofcombining inference and search to effectively solve ADCOPs at an acceptableloss of privacy. Specifically, we propose a hybrid complete algorithm calledPT-ISABB which uses a tailored inference algorithm to provide tight lowerbounds and a tree-based complete search algorithm to exhaust the search space.We prove the correctness of our algorithm and the experimental resultsdemonstrate its superiority over other state-of-the-art complete algorithms.

PT-ISABB: A Hybrid Tree-based Complete Algorithm to Solve Asymmetric Distributed Constraint Optimization Problems