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A complete approach to reasoning under uncertainty requires support forincremental and interactive formulation and revision of, as well as reasoningwith, models of the problem domain capable of representing our uncertainty. Wepresent a hybrid reasoning scheme which combines symbolic and numeric methodsfor uncertainty management to provide efficient and effective support for eachof these tasks. The hybrid is based on symbolic techniques adapted fromAssumption-based Truth Maintenance systems (ATMS), combined with numericmethods adapted from the Dempster/Shafer theory of evidence, as extended inBaldwin's Support Logic Programming system. The hybridization is achieved byviewing an ATMS as a symbolic algebra system for uncertainty calculations. Thistechnique has several major advantages over conventional methods for performinginference with numeric certainty estimates in addition to the ability todynamically determine hypothesis spaces, including improved management ofdependent and partially independent evidence, faster run-time evaluation ofpropositional certainties, the ability to query the certainty value of aproposition from multiple perspectives, and the ability to incrementally extendor revise domain models.

Combining Symbolic and Numeric Approaches to Uncertainty Management