Belgian authorities say they have arrested three people in connection with a plot to attack Prime Minister Bart De Wever and other politicians using drone-mounted explosives. Federal prosecutor Ann Fransen announced the arrests on Thursday and said the group were under investigation for an "attempted terrorist murder and participation in the activities of a terrorist group", according to Belgian public broadcaster RTBF. "There are also indications that the suspects aimed to construct a drone to which a payload could be attached," she added. Fransen did not name their intended targets, but social media posts from senior figures in De Wever's government indicate that he was on the list. "The news of a planned attack targeting Prime Minister Bart De Wever is deeply shocking," wrote Deputy Prime Minister Maxime Prevot in a post on X. "I express my full support to the Prime Minister, his wife, and his family, as well as my gratitude to the security and justice services whose swift action prevented the worst."

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.

The notion of bounded rationality originated from the insight that perfectly rational behavior cannot be realized by agents with limited cognitive or computational resources. Research on bounded rationality, mainly initiated by Herbert Simon, has a longstanding tradition in economics and the social sciences, but also plays a major role in modern AI and intelligent agent design. Taking actions under bounded resources requires an agent to reflect on how to use these resources in an optimal way - hence, to reason and make decisions on a meta-level. In this paper, we will look at automated machine learning (AutoML) and related problems from the perspective of bounded rationality, essentially viewing an AutoML tool as an agent that has to train a model on a given set of data, and the search for a good way of doing so (a suitable "ML pipeline") as deliberation on a meta-level.