IngentaConnect Adaptive dynamic probabilistic networks for distributed uncertain...

Authors: Shi, Dongyu¹; You, Jinyuan¹

Source: Journal of Experimental & Theoretical Artificial Intelligence, Volume 19, Number 4, December 2007 , pp. 269-284(16)

Publisher: Taylor and Francis Ltd

Abstract:

Uncertainty processing is a core task in many applications of distributed systems. Typical distributed systems have local processing nodes to collect information, which usually contain uncertainties, and do computational work. The nodes can interact with each other, and they evolve with time. A promising way of modelling and processing uncertainties in these systems is to use graphical models to form beliefs about the required information. Dynamic probabilistic networks for distributed uncertainty processing are presented in this paper. Two approaches are given, and comparison shows that the model with the more state-of-art approach performs better. Since it is not possible to obtain enough knowledge to construct an exact model at the beginning, the model needs to adjust itself when evolving. Therefore we have developed a parameter update algorithm to make the model adapt to the changing environment. Experiments are presented to show the effectiveness of the models and the algorithms.

Keywords: Dynamic probabilistic network; Distributed uncertainty processing; CTBN; DBN; Belief propagation and updating; Parameter updating

Document Type: Research article

DOI: 10.1080/13642530701197710

Affiliations: 1: Department of Computer Science and Engineering, Jiao Tong University, Shanghai 200030, P.R. China

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Adaptive dynamic probabilistic networks for distributed uncertainty processing