Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Information entropy and structural metrics based estimation of situations as a basis for situation awareness and decision support
 Institute of Electrical and Electronics Engineers IEEE; IEEE Communications Society: IEEE International MultiDisciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support, CogSIMA 2012 : March 68, 2012 in New Orleans, LA, USA Piscataway/NJ: IEEE, 2012 ISBN: 9781467303453 ISBN: 9781467303439 (Print) S.111116 
 International MultiDisciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) <2012, New Orleans/La.> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IOSB () 
Abstract
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 subsystem 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 DegreeofBelief (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 subsystems. 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.