A Nonmonotonic Logic-Based Method for Model Management
一种基于非单调逻辑的模型管理方法

This paper presents a nonmonotonic logic-based approach to the problem of model management in uncertain environments. The proposed algorithm for uncertainty analysis provides a mechanism for uncertainty representation, propagation, evidence combination and problem solving under uncertainties. Furthermore, uncertainties about model structures, i.e., the probabilities of possible model structures, are represented by probability propositions supported by sets of ATMS's assumptions about uncertain situations. Hence, the management of uncertain causal link between models is realized in the processes of truth maintenance, belief assignment and adjustment about assumptions' environment, wherein, conflict situations presented in problem solving processes and related ATMS justifications about the results of problem solving are taken as new evidences for truth maintenance and the adjustment of degrees of belief.