Abstract: This paper introduces an information theoretic approach to verification of causal models in modular Bayesian fusionsystems. We assume distributed fusionsystems which are gradually extended by adding new modules, each having a limited domain knowledge captured in local Bayesian networks. However, since dierent modules originate from different, independent design processes important dependencies between the variables in different modules might not correctly be captured in the distributed fusion system. This could have a significant impact on the fusion quality. The introduced
method supports discovery of significant dependencies which are ignored in the distributed fusion system.
Abstract: We introduce Distributed perception networks (DPNs), a distributed architecture for efficient and reliable fusion of large quantities of heterogeneous and noisy information. DPNs consist of agents, processing nodes with limited fusion capabilities, which cooperate and can autonomously form arbitrarily large distributed classifiers. DPNs are based on causal models, which often facilitate analysis, design and maintenance of complex informationfusionsystems. This is possible because
observations obtained from different information sources often result from causal processes which in turn can be modeled with relatively simple, yet mathematically rigorous and compact probabilistic causal models. Such models, in turn, facilitate
decentralized world modeling and informationfusion.