Widulle, Niklas

The protection of non-combatants in times of autonomous warfare raises the question of the timeliness of the international protective emblem. (Fully) Autonomous weapon systems are often launched from a great distance, and there may be no possibility for the operators to notice protective emblems at the point of impact; therefore, such weapon systems will need to have a way to detect protective emblems and react accordingly. In this regard, the present contribution suggests a cross-frequency protective emblem. Technical deployment is considered, as well as interpretation by methods of machine learning. Approaches are explored as to how software can recognize protective emblems under the influence of various boundary conditions. Since a new protective emblem could also be misused, methods of distribution are considered, including encryption and authentication of the received signal. Finally, ethical aspects are examined.

Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsulate production functions as services have shown to be promising in order to increase the flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding a production plan based on provided functionalities for a demanded product, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. While there is a variety of approaches to production planning, flexible production poses specific requirements that are not covered by existing research. In this contribution, we first capture these requirements for flexible production environments. Afterwards, an overview of current Artificial Intelligence approaches that can be utilized in order to overcome the aforementioned challenges is given. For this purpose, we focus on planning algorithms, but also consider models of production systems that can act as inputs to these algorithms. Approaches from both symbolic AI planning as well as approaches based on Machine Learning are discussed and eventually compared against the requirements. Based on this comparison, a research agenda is derived.