Integrated modular Bayesian networks with selective inference for context-aware decision making

As many devices equipped with various sensors have recently proliferated, the fusion methods of various information and data from different sources have been studied. Bayesian network is one of the popular methods that solve this problem to cope with the uncertainty and imprecision. However, because a monolithic Bayesian network has high computational and design complexities, it is hard to apply to realistic problems. In this paper, we propose a modular Bayesian network system to extract context information by cooperative inference of multiple modules, which guarantees reliable inference compared to a monolithic Bayesian network without losing its strength like the easy management of knowledge and scalability. The proposed method preserves inter-modular dependencies by virtual linking and has lower computational complexity in complicated environments. The inter-modular d-separation controls local information to be delivered only to relevant modules. We verify that the proposed modular Bayesian network is enough to keep inter-modular causalities in a time-saving manner. This paper shows a possibility that a context-aware system would be easily constructed by mashing up Bayesian network fractions independently designed or leaned in different domains.