Blackboard-based Sensor Interpretation using a Symbolic Model of the Sources of Uncertainty in Abductive Inferences (original) (raw)

Sensor interpretation involves the determination of high-level explanations of sensor data. The interpretation process is based on the use of abduction. Interpretation systems incrementally construct hypotheses using abductive inferences to identify possible explanations for the data and, conversely, possible support for the hypotheses. We have developed and implemented a new blackboard-based interpretation framework called RESUN. One of the key features of RESUN is that it uses a model of the sources of uncertainty in abductive interpretation inferences to create explicit, symbolic representations (called SOUs) of the reasons why hypotheses are uncertain. The symbolic SOUs make it possible for the system to understand the reasons why its hypotheses are uncertain so that it can dynamically select the most appropriate methods for resolving uncertainty. Our model of uncertainty de nes a set of classes of SOUs that are applicable to interpretation problems which can be posed as abduction problems. Each interpretation application may require slightly different instances of each of the classes of SOUs to best represent uncertainty. We have implemented the RESUN framework using a simulated aircraft monitoring system and have run experiments that demonstrate how the SOUs enable the use of more e ective interpretation strategies. To verify the generality of the approach, we are also using RESUN to implement a sound understanding testbed.