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Qualitative probabilistic network-based fusion of time-series uncertain knowledge

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Abstract

In time-series environments, uncertain knowledge among variables in a time slice can be represented and modeled by a Bayesian network (BN). In this paper, we are to achieve the global uncertain knowledge during a period of time for decision-making or action selection by fussing or combining the participating uncertainties of multiple time slices consistently while satisfying the demands of high efficiency and instantaneousness. We adopt qualitative probabilistic network (QPN), the qualitative abstraction of BN, as the underlying framework of modeling and fusing time-series uncertain knowledge. The BNs in continuous time slices constitute time-series BNs, from which we derive time-series QPNs. Taking time-series BNs as input, we propose a QPN-based approach to fuse time-series uncertainties in line with temporal specialties. First, for each time slice, we enhance the implied QPN by augmenting interval-valued weights derived from the corresponding BN, and then obtain the QPN with weighted influences, denoted EQPN (Enhanced Qualitative Probabilistic Network), which provides a quantitative and conflict-free basis for fusing uncertain knowledge. Then, we give the method for fusing the graphical structures of time-series EQPNs based on the concept of Markov equivalence. Following, we give a superposition method for fusing qualitative influences of time-series EQPNs. Experimental results show that our method is not only efficient, but also effective. Meanwhile, the simulation results when applying time-series EQPNs and the fusion algorithm to a robotic system show that our method is applicable in realistic intelligent situations.

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Notes

  1. An undirected graph is chordal if every cycle of length \(n\;=\;4\) possesses a chord (Andersson et al. 1997; Pearl 1988).

  2. A component is the connected subgraph generated by removing the orientations in a DAG (Pearl 1988).

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61163003, 61232002, 71161015), the National Basic Research (973) Program of China (No. 2010CB328106), the Yunnan Provincial Foundation for Leaders of Disciplines in Science and Technology (No. 2012HB004), the Natural Science Foundation of Yunnan Province (No. 2013FB010), and the Research Foundation of Key Laboratory of Software Engineering of Yunnan Province (No. 2012SE013).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China

    Kun Yue, Wenhua Qian & Weiyi Liu

  2. Key Laboratory of Software Engineering of Yunnan Province, Kunming, China

    Kun Yue & Jin Li

  3. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Xiaodong Fu

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  1. Kun Yue
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  2. Wenhua Qian
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  3. Xiaodong Fu
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  4. Jin Li
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  5. Weiyi Liu
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Correspondence to Kun Yue.

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Communicated by V. Loia.

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Yue, K., Qian, W., Fu, X. et al. Qualitative probabilistic network-based fusion of time-series uncertain knowledge. Soft Comput 19, 1953–1972 (2015). https://doi.org/10.1007/s00500-014-1381-y

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