Decision Making and Uncertainty Management in a 3D Reconstruction System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
IEEE Transactions on Mobile Computing
Landmark detection from mobile life log using a modular Bayesian network model
Expert Systems with Applications: An International Journal
A multi-agent systems approach to distributed bayesian information fusion
Information Fusion
Reduction of computational complexity in Bayesian networksthrough removal of weak dependences
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Geo referenced dynamic bayesian networks for user positioning on mobile systems
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Active affective State detection and user assistance with dynamic bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Information integration via hierarchical and hybrid bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Ubiquitous computing brings various information and knowledge derived from different sources, under which Bayesian networks are widely used to cope with the uncertainty and imprecision. 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 the monolithic Bayesian network without losing its strength like the ease of management of knowledge and scalability. Moreover, to provide a lightweight updating method for highly complicated environment, we propose a novel method of preserving inter-module dependencies by linking modules virtually, which extends d-separation to an inter-modular concept to control local information to be delivered only to relevant modules. Experimental results show that the proposed modular Bayesian networkscan keep inter-modular causalities in a time-saving manner. This paper implies that a context-aware system can be easily developed by exploiting Bayesian network fractions independently designed or learned in many domains.