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Cooperative triangulation in MSBNs without revealing subnet structures
Author(s) -
Xiang Y.
Publication year - 2001
Publication title -
networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.977
H-Index - 64
eISSN - 1097-0037
pISSN - 0028-3045
DOI - 10.1002/1097-0037(200101)37:1<53::aid-net5>3.0.co;2-h
Subject(s) - correctness , computer science , inference , theoretical computer science , interpretation (philosophy) , subnet , triangulation , set (abstract data type) , markov chain , graph , dependency graph , algorithm , artificial intelligence , mathematics , machine learning , computer network , geometry , programming language
Multiply sectioned Bayesian networks (MSBNs) provide a coherent framework for probabilistic inference in a cooperative multiagent distributed interpretation system. Inference in MSBNs can be performed effectively using a compiled representation. The compilation involves the triangulation of the collective dependency structure (a graph) defined in terms of the union of a set of local dependency structures (a set of graphs). Privacy of agents eliminates the option to assemble these graphs at a central location and to triangulate their union. Earlier work solved distributed triangulation in a restricted case. The method is conceptually complex and the correctness of its extension to the general case is difficult to justify. In this paper, we present a new method that is conceptually simpler and is efficient. We prove its correctness in the general case and demonstrate its performance experimentally. © 2001 John Wiley & Sons, Inc.