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

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.