In this article, we examine the state-of-the-art and current applications of artificial intelligence (AI), specifically for human resources (HR). We study whether, due to the experimental state of the algorithms used and the nature of training and test samples, a further control and auditing in the research community is necessary to guarantee fair and accurate results. In particular, we identify the positive and negative consequences of the usage of video-interview analysis via AI in recruiting processes as well as the main machine learning techniques used and their degrees of efficiency. We focus on some controversial characteristics that could lead to ethical and legal consequences for candidates, companies and states regarding discrimination in the job market (e.g. gender and race). There is a lack of regulation and a need for external and neutral auditing for the type of analyses done in interviews. We present a multi-agent architecture that aims at total legal compliance and more effective HR processes management.

In this paper, we consider the route coordination problem in emergency evacuation of large smart buildings. The building evacuation time is crucial in saving lives in emergency situations caused by imminent natural or man-made threats and disasters. Conventional approaches to evacuation route coordination are static and predefined. They rely on evacuation plans present only at a limited number of building locations and possibly a trained evacuation personnel to resolve unexpected contingencies. Smart buildings today are equipped with sensory infrastructure that can be used for an autonomous situation-aware evacuation guidance optimized in real time. A system providing such a guidance can help in avoiding additional evacuation casualties due to the flaws of the conventional evacuation approaches. Such a system should be robust and scalable to dynamically adapt to the number of evacuees and the size and safety conditions of a building. In this respect, we propose a distributed route recommender architecture for situation-aware evacuation guidance in smart buildings and describe its key modules in detail. We give an example of its functioning dynamics on a use case.

Fleet management systems are commonly used to coordinate mobility and delivery services in a broad variety of domains. However, their traditional top-down control architecture becomes a bottleneck in open and dynamic environments, where scalability, proactiveness, and autonomy are becoming key factors for their success. Here, the authors present an abstract event-based architecture for fleet management systems that supports tailoring dynamic control regimes for coordinating fleet vehicles, and illustrate it for the case of medical emergency management. Then, they go one step ahead in the transition toward automatic or driverless fleets, by conceiving fleet management systems in terms of cyber-physical systems, and putting forward the notion of cyber fleets.