Policy support for autonomous swarms of drones

In recent years drones have become more widely used in military and non-military applications. Automation of these drones will become more important as their use increases. Individual drones acting autonomously will be able to achieve some tasks, but swarms of autonomous drones working together will be able to achieve much more complex tasks and be able to better adapt to changing environments. In this paper we describe an example scenario involving a swarm of drones from a military coalition and civil/humanitarian organisations that are working collaboratively to monitor areas at risk of flooding. We provide a definition of a swarm and how they can operate by exchanging messages. We define a flexible set of policies that are applicable to our scenario that can be easily extended to other scenarios or policy paradigms. These policies ensure that the swarms of drones behave as expected (e.g., for safety and security). Finally we discuss the challenges and limitations around policies for autonomous swarms and how new research, such as generative policies, can aid in solving these limitations

FactRunner: A new system for NLP-based information extraction from wikipedia

Wikipedia is playing an increasing role as a source of humanreadable knowledge, because it contains an enormous amount of high quality information written by human authors. Finding a relevant piece of information in this huge collection of natural language text is often a time-consuming process, as a keyword-based search interface is the main method for querying. Therefore, an iterative process to explore the document collection to find the information of interest is required. In this paper, we present an approach to extract structured information from unstructured documents to enable structured queries. Information Extraction (IE) systems have been proposed for this tasks, but due to the complexity of natural language, they often produce unsatisfying results. As Wikipedia contains, in addition to the plain natural language text, links between documents and other metadata, we propose an approach which exploits this information to extract more accurate structured information. Our proposed system FactRunner focusses on extracting structured information from sentences containing such links, because the links may indicate more accurate information than other sentences. We evaluated our system with a subset of documents from Wikipedia and compared the results with another existing system. The results show that a natural language parser combined with Wikipedia markup can be exploited for extracting facts in form of triple statements with a high accuracy