Abstract
In this paper, we uncover some relationships between local consistency in constraint networks and message passing akin to belief propagation in probabilistic reasoning. We develop a new message passing algorithm, called the min-max message passing (MMMP) algorithm, for unifying the different notions of local consistency in constraint networks. In particular, we study its connection to arc consistency (AC) and path consistency. We show that AC-3 can be expressed more intuitively in the framework of message passing. We also show that the MMMP algorithm can be modified to enforce path consistency.
The research at the University of Southern California was supported by NSF under grant numbers 1409987 and 1319966.
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Xu, H., Kumar, T.K.S., Koenig, S. (2017). Min-Max Message Passing and Local Consistency in Constraint Networks. In: Peng, W., Alahakoon, D., Li, X. (eds) AI 2017: Advances in Artificial Intelligence. AI 2017. Lecture Notes in Computer Science(), vol 10400. Springer, Cham. https://doi.org/10.1007/978-3-319-63004-5_27
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DOI: https://doi.org/10.1007/978-3-319-63004-5_27
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