In a cognitive reasoning system, the four-stage Observe-Orient-Decision-Act (OODA) reasoning loop is of interest. The OODA loop is essential for the situational awareness especially in heterogeneous data fusion. Cognitive reasoning for making decisions can take advantage of different formats of information such as symbolic observations, various real-world sensor readings, or the relationship between intelligent modalities. Markov Logic Network (MLN) provides mathematically sound technique in presenting and fusing data at multiple levels of abstraction, and across multiple intelligent sensors to conduct complex decision-making tasks. In this paper, a scenario about vehicle interaction is investigated, in which uncertainty is taken into consideration as no systematic approaches can perfectly characterize the complex event scenario. MLNs are applied to the terrestrial domain where the dynamic features and relationships among vehicles are captured through multiple sensors and information sources regarding the data uncertainty.
Jingyang Lu, Bin Jia, Genshe Chen, Hua-mei Chen, Nichole Sullivan, Khanh Pham, and Erik Blasch, "Markov logic network based complex event detection under uncertainty," Proc. SPIE 10641, Sensors and Systems for Space Applications XI, 106410C (Presented at SPIE Defense + Security: April 16, 2018; Published: 2 May 2018); https://doi.org/10.1117/12.2305206.
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