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RUM (Reasoning with Uncertainty Module), is an integrated software tool basedon a KEE, a frame system implemented in an object oriented language. RUM'sarchitecture is composed of three layers: representation, inference, andcontrol. The representation layer is based on frame-like data structures thatcapture the uncertainty information used in the inference layer and theuncertainty meta-information used in the control layer. The inference layerprovides a selection of five T-norm based uncertainty calculi with which toperform the intersection, detachment, union, and pooling of information. Thecontrol layer uses the meta-information to select the appropriate calculus foreach context and to resolve eventual ignorance or conflict in the information.This layer also provides a context mechanism that allows the system to focus onthe relevant portion of the knowledge base, and an uncertain-belief revisionsystem that incrementally updates the certainty values of well-formed formulae(wffs) in an acyclic directed deduction graph. RUM has been tested andvalidated in a sequence of experiments in both naval and aerial situationassessment (SA), consisting of correlating reports and tracks, locating andclassifying platforms, and identifying intents and threats. An example of navalsituation assessment is illustrated. The testbed environment for developingthese experiments has been provided by LOTTA, a symbolic simulator implementedin Flavors. This simulator maintains time-varying situations in a multi-playerantagonistic game where players must make decisions in light of uncertain andincomplete data. RUM has been used to assist one of the LOTTA players toperform the SA task.

Using T-Norm Based Uncertainty Calculi in a Naval Situation Assessment Application