This work advocate self-explanation as one foundation of self-* properties. Arguing that for system component to become more self-explanatory the underlining foundation is an awareness of themselves and their environment. In the research area of adaptive software, self-* properties have shifted into focus caused by the tendency to push ever more design decisions to the applications runtime. Thus fostering new paradigms for system development like intelligent and learning agents. This work surveys the state-of-the-art methods of self-explanation in software systems and distills a definition of self-explanation. Additionally, we introduce a measure to compare explanations and propose an approach for the first steps towards extending descriptions to become more explanatory. The conclusion shows that explanation is a special kind of description. The kind of description that provides additional information about a subject of interest and is understandable for the audience of the explanation. Further the explanation is dependent on the context it is used in, which brings about that one explanation can transport different information in different contexts. The proposed measure reflects those requirements.

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Semantic information is considered as foundation upon which modern approaches attempt to tackle the challenges of dynamic environments-service orchestration and ontology matching are two examples for the use of such information. Yet, many developers avoid the additional effort of adding semantic information (e.g., through annotations) to their data sets-limiting the reusability and interoperability of their Apps, services, or data. This problem is called the "knowledge acquisition bottleneck", which can be addressed by providing suitable tool support. This survey analyses the state-of-the-art of such tools that support developers in the task of semantically enriching entities. Providing an overview of available tools from the early days until now, we particularly focus on the 'level of automation'. Concluding that automation is very limited in contemporary tools we propose a concept that mixes connectionist and symbolic representation of meaning to decrease the manual effort.

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Due to the fact that electric vehicles have not broadly entered the vehicle market there are many attempts to convince producers to integrate technologies that utilise embedded batteries for purposes different from driving. The vehicle-to-grid technology, for instance, literally turns electric vehicles into a mobile battery, enabling new areas of applications (e.g., to provide regulatory energy, to do grid-load balancing, or to buffer surpluses of energy) and business perspectives. Utilising a vehicle's battery, however is not without a price-in this case: the driver's mobility. Given this dependency, it is interesting that most available works consider the application of electric vehicles for energy and grid-related problems in isolation, that is, detached from mobility-related issues. The distributed artificial intelligence laboratory, or DAI-Lab, is a thirdparty funded research lab at Technische Universität Berlin and integrates the chair for agent technologies in business applications and telecommunication. The DAI-Lab has engaged in a large number of both, past and upcoming projects concerned with two aspects of managing electric vehicles, namely: energy and mobility. This article aims to summarise experiences that were collected during the last years and to present developed solutions which consider energy and mobility-related problems jointly.

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