Computing Confidences in Tactical Rule-Based Systems by Using Dempster- Shafer Theory

This report describes the implementation of confidence computing methods based on Dempster-Shafer Theory. The theory is applicable to tactical decision problems that can be formulated in terms of sets of mutually exclusive and exhaustive propositions. Dempster's combining procedure, a generalization of Bayesian inference, is used to combine probability mass assignments supplied by independent bodies of evidence. The computing procedures are implemented in the rule-based system ROSIE, and apply to all valid mass assignments. An ordering strategy is used to combine various kinds of assignments by using different procedures that exploit the special features of each. Applications to platform typing and contact association are demonstrated.