A General Purpose Inference Engine for Evidential Reasoning Research
This work addresses uncertainty representation for automated reasoning researchers, but it appears incremental as it builds on prior studies without claiming major breakthroughs.
The paper tackles the problem of representing and manipulating uncertainty in automated reasoning systems by developing a general-purpose inference engine to extend previous experiments with uncertainty calculi in document retrieval.
The purpose of this paper is to report on the most recent developments in our ongoing investigation of the representation and manipulation of uncertainty in automated reasoning systems. In our earlier studies (Tong and Shapiro, 1985) we described a series of experiments with RUBRIC (Tong et al., 1985), a system for full-text document retrieval, that generated some interesting insights into the effects of choosing among a class of scalar valued uncertainty calculi. [n order to extend these results we have begun a new series of experiments with a larger class of representations and calculi, and to help perform these experiments we have developed a general purpose inference engine.