Dealing with Uncertainty in Situation Assessment: towards a Symbolic Approach

TL;DR

This paper explores a symbolic framework for handling uncertainties in situation assessment, focusing on object, condition, activity, and plan recognition from sensor data, by adapting numerical estimation tools to symbolic contexts.

Contribution

It introduces a purely symbolic approach to manage uncertainties in situation assessment, extending classical numerical estimation methods to symbolic models.

Findings

Identifies three types of uncertainties in situation assessment.

Demonstrates symbolic uncertainties can be addressed with adapted numerical tools.

Provides a framework for purely symbolic uncertainty management.

Abstract

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.