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Speeding Up Incomplete GDL-based Algorithms for Multi-agent Optimization with Dense Local Utilities
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Incomplete GDL-based algorithms including Max-sum and its variants are important methods for multi-agent optimization. However, they face a significant scalability challenge as the computational overhead grows exponentially with respect to the arity of each utility function. Generic Domain Pruning (GDP) technique reduces the computational effort by performing a one-shot pruning to filter out suboptimal entries. Unfortunately, GDP could perform poorly when dealing with dense local utilities and
doi:10.24963/ijcai.2020/5
dblp:conf/ijcai/DengA20
fatcat:eqmrd4v4tzabxn4ge6yll7gia4