Norman Carver and Victor Lesser

Sensor interpretation involves the determination of high-level explanations of sensor data. Blackboardbased interpretation systems have usually been limited to incre,mental hypothesize and test strategies for resolving uncertainty. We have developed a new interpretation framework that supports the use of more sophisticated strategies like differential diagnosis. The RESUN framework has two key components: an evidential representation that includes explicit, symbolic encodings of the sources of uncertainty (SOUs) in the evidence for hypotheses and a script-based, incremental control planner. Interpretation is viewed as an incremental process of gathering evidence to resolve particular sources of uncertainty. Control plans invoke actions that examine the symbolic SOUs associated with hypotheses and use the resulting information to post goals to resolve uncertainty. These goals direct the system to expand methods appropriate for resolving the current sources of uncertainty in the hypotheses. The planner’s refocusing mechanism makes it possible to postpone focusing decisions when there is insufficient information to make decisions and provides opportunistic control capabilities, The RESUN framework has been implemented and experimentally verified using a simulated aircraft monitoring application.