Arms control for armed uninhabited vehicles: an ethical issue

Arming uninhabited vehicles (UVs) is an increasing trend. Widespread deployment can bring dangers for arms-control agreements and international humanitarian law (IHL). Armed UVs can destabilise the situation between potential opponents. Smaller systems can be used for terrorism. Using a systematic definition existing international regulation of armed UVs in the fields of arms control, export control and transparency measures is reviewed; these partly include armed UVs, but leave large gaps. For preventive arms control a general prohibition of armed UVs would be best. If that is unattainable, several measures should be taken. An explicit prohibition of autonomous attack, that is without a human decision, should be added to IHL. Concerning armed UVs remotely controlled by a human soldier, recommendations differ according to type or mission. New kinds of uninhabited nuclear-weapon carriers should be banned. Space weapons should be prohibited in general. UVs smaller than 0.2–0.5 m should be banned. Bigger remotely controlled armed UVs not equipped with weapons of mass destruction should be subject to numerical limitations in various categories. For these the Treaty on Conventional Armed Forces in Europe is an important precedent.     

Efficient reverse k-nearest neighbor estimation

The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. In addition, many applications require a fast approximate answer of RkNN-queries. For these scenarios, it is important to generate a fast answer with high recall. In this paper, we propose the first approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use an approximation of the nearest-neighbor-distances in order to prune the search space. We show that our method scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.