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