Proceedings of the IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support, 2012.
IEEE Second International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), New Orleans, USA, March 6 - 8, 2012
Modern autonomous systems are challenged by complex, overwhelming computer processing power, though, time critical tasks. Handling of reactive and proactive activities in real time requires an exceptionally well designed autonomous system for constant situation awareness and decision support. The basis for such situation awareness and decision support 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. It receives information from sensors, processes it and fuses into existing environment description. Since the incoming information contains uncertainties and can be treated, for example, by means of Degree-of-Belief (DoB) distributions, powerful statistical methods can be employed for the information fusion process (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, for example, 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. Both information flow and modeling situation can be evaluated numerically with the information entropy calculation. The difference between entropies of an attribute before and after the observation fusion gives a numerical estimation for the information gain. On the other hand, the evaluation of entropies of all attributes can give an overall estimation of the object representation. Extending entropy analysis on groups of objects and their relations allows for numerical estimation of situations. 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 random variables and the entropy of discrete 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. The proposed analysis was developed within the German Research Foundation (DFG) Collaborative Research Center (SFB) 588 ``Humanoid Robots -- Learning and Cooperating Multimodal Robots''. The main goal of the project is to build a humanoid assisting robot. For development and tests, a kitchen environment has been created as a test field. Within this environment, several humanoid robots are cooperating with humans and performing complex tasks, e.g. interactive objects and concepts learning, context recognition, analysis of situations and intentions.