Dealing with Uncertainty in Situation Assessment: towards a Symbolic Approach
This work addresses uncertainty issues in situation assessment for applications like object and activity recognition, but it appears incremental as it adapts existing tools to a symbolic context.
The paper tackles uncertainty in situation assessment by identifying three types of uncertainties and distinguishing between numerical and symbolic cases, focusing on handling symbolic uncertainties within a symbolic framework through transposition of classical numerical estimation tools.
The situation assessment problem is considered, in terms of object, condition, activity, and plan recognition, based on data coming from the real-word {em via} various sensors. It is shown that uncertainty issues are linked both to the models and to the matching algorithm. Three different types of uncertainties are identified, and within each one, the numerical and the symbolic cases are distinguished. The emphasis is then put on purely symbolic uncertainties: it is shown that they can be dealt with within a purely symbolic framework resulting from a transposition of classical numerical estimation tools.