On adaptive open-world modeling based on information fusion and inductive inference

In this technical report, a conception for adaptive open-world modeling for cognitive information systems is presented. In cognitive systems, a world model serves as information storage for sensor data and thus represents an abstract, simplified copy of the observed environment. In order to allow for a high-level information processing on a semantic layer, the represented objects are backed by a semantically enriched domain model containing a priori knowledge. Such prior knowledge generally contains only a fixed number of object concepts, thus constituting a closed-world model. However, in many real-life applications, the considered environment is not closed. For coping with changing environments, a cognitive system must be equipped with an adaptive world model able to adjust to an observed open environment. For designing such an open-world model, this report evaluates and summarizes information fusion and concept learning techniques