Will-beset by IN -TECH Representatives Concepts acquiring information vi su al iz in g

No abstract

The development and usage of Unmanned Aerial Vehicles (UAVs) quickly increased in the last decades, mainly for military purposes. This technology is also now of high interest in non-military contexts like logistics, environmental studies and different areas of civil protection. While the technology for operating a single UAV is rather mature, additional efforts are still necessary for using UAVs in fleets (or swarms). The Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques (ASIMUT) project which is supported by the European Defence Agency (EDA) aims at investigating and demonstrating dedicated surveillance services based on fleets of UAVs. The aim is to enhance the situation awareness of an operator and to decrease his workload by providing support for the detection of threats based on multi-sensor multi-source data fusion. The operator is also supported by the combination of information delivered by the heterogeneous swarms of UAVs and by additional information extracted from intelligence databases. As a result, a distributed surveillance system increasing detection, high-level data fusion capabilities and UAV autonomy is proposed

World modeling can provide environment information to applications for decision support and situation assessment. In a semantic world model like the Object-Oriented World Model (OOWM), knowledge about an application domain is modeled a priori. In practice, however, world modeling systems have to deal with an open world, where unforeseen real-world entities can occur during operations. To enable open-world modeling for the OOWM, an approach to adaptive knowledge management is presented. This approach proposes an information-theoretic model evaluation based on the Minimum Description Length principle

As a prerequisite to supporting situation assessment and awareness tasks, information about considered situations must be adequately represented in respective support systems. A meaningful representation can be provided by world modeling systems like the Object-Oriented World Model (OOWM). The OOWM models the current state of an application domain based on observed information while relying on domain knowledge for adding semantics to the model. This domain knowledge is usually designed a priori by human experts. Yet, in complex real-life domains, the occurrence of a priori not considered domain entities is likely during system operations. Therefore, the OOWM has to be able to deal with a potentially open world - by adapting the domain model in reaction to current and past circumstances. To this end, an approach for adaptive knowledge management has previously been proposed. Extending the approach, this contribution introduces a holistic process for managing and dealing with unforeseen entities supporting model adaptation and details a method for acquiring new concept definitions based on poorly represented entities