Information entropy and structural metrics based estimation of situations as a basis for situation awareness and decision support

Modern autonomous systems are challenged by complex, overwhelming computer processing power, though, time critical tasks. The basis for performing such tasks is a robust and comprehensive representation of the environment of the autonomous system, called world modeling. The world modeling sub-system is responsible for a representation of the current state of the environment, as well as a history of past states and forecasts for possible future states. The incoming sensory information is contaminated by uncertainties and, thus, is represented in form of probability distributions that can be treated by means of Degree-of-Belief (DoB). These DoB distributions are fused into existing environment description within the world modeling by statistical methods, e.g. Bayesian fusion. The history of past states allows for advanced information analysis, such as qualitative situation estimation. On the other hand, a direct analysis of the DoB distributions, for example, information entropy calculation, gives a quantitative estimation of situations. The future states can be predicted on the basis of known evolution parameters of the environment, i.e. by attributes and objects aging modeling. The qualitative and quantitative situation estimations, as well as the comprehensive environment description itself allows for permanent situation awareness and intelligent support for decision making sub-systems. In order to numerically estimate attribute sets of all modeling objects, the entropy calculation must be unified for both discrete and continuous DoB cases. In order to overcome the infinite discrepancy between the entropy of quantized and continuous random variables, the unification introduces a notion of the least discernible quantum (LDQ). The LDQ defines the utmost precision for any operation over the attribute