Abstract
Qualitative propagation influences in qualitative inferences are unlike and interrelated on the different hierarchy of knowledge granules, and quantitative information loss easily results in reasoning conflicts. This paper presents a hierarchical qualitative inference model with substructures which to some extent can eliminate the qualitative impact of uncertainty and solve trade-off problems by metastructures with basic decomposition and coarse-grained mesoscale substructures with edge-deletion. The substructural inferences could not only reduce computational complexity, but provide an approximate strategy for modular reasoning on large-scale problems. The example respectively illustrates the two substructural methods are both effective.
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Zhang, Z., Miao, D., Qian, J. (2011). Hierarchical Qualitative Inference Model with Substructures. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_94
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DOI: https://doi.org/10.1007/978-3-642-24425-4_94
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