## 3 Situated Systems  
In [13] the authors approach the problem of _coordinated control_ of a fleet of autonomous hovercrafts at both the individual and the collective level: for the former, they propose a controller based on a Radial Basis Function Neural Network for tolerating non-modelled terms while following a given path, for the latter they apply _multi-agent consensus_ to achieve movement in a desired formation. Simulations of the approach confirm effectiveness in a few selected communication topologies, such as cascade-directed communication graph and parallel-directed communication graph. In [14] the authors apply _multi-agent based modelling_ to the domain of _urban planning_, in particular, for supporting decision making about the design and deployment of an electric charging stations infrastructure in a city. The proposed multi-agent system features several agents in charge of complementary functionalities, such as querying open data portals of local administrations to gather info about potential offer and demand for charging stations as well as average traffic conditions, crawling social networks to rank potential locations where to put charging stations, and execute optimisation algorithms to find the best spots among a set of candidates. A graphical interface is also available to set various parameters guiding the optimisation process, which is based on _genetic algorithms_. The authors validate their approach with a case study implemented in the city of Valencia, Spain. Also [15] concerns the domain of urban planning: there, the authors propose a multi-agent system providing decision support, in the form of _demand prediction services_, in the context of a _bike sharing_ application. The system analyses heterogeneous data such as availability of bikes at stations, trip information, and weather forecasts to build a demand prediction model and a dashboard for historical data visualisation. The prediction model is built by comparing regression techniques such as Random Forest and Gradient Boosting, evaluated by means of the Root Mean Square Logarithmic Error. The resulting system has been deployed to the city of Salamanca, Spain. In [16] the authors exploit _agent-based modelling_ and simulation techniques to investigate how different agent behaviours and interactions affect the _negotiation process_ in a car-pooling scenario. The proposed multi-agent system adopts an organisational metaphor to arrange and analyse the social relationships and individual behaviour of agents; in particular, the _Capacity, Role, Interaction, and Organization_ meta-model [17]. The proposed system works in three main phases: _exploration_, during which agents search the carpooling social network seeking for opportunities to carpool, either as drivers or passengers, and get matched depending on user profiles, trip data, time of constraints, etc. ; _negotiation_, where agents being matched start a negotiation process to fine-tune the details of the joint voyage, such as the actual departure time, the path to be followed, and the driver; _carpooling_, where the actual trip takes place and contingencies should be handled (such as disbanding non-compliant agents). To validate the approach, the FEATHERS operative activity-based traffic demand model is used to generate synthetic data and agents in order to test different negotiation settings. In [18] the authors propose a novel _navigational strategy_ for moving robots able to avoid collision with multiple passive agents featuring a (at most) partially predictable behaviour. The main application scenario envisioned is safe autonomous driving in presence of pedestrians, and the approach proposed--featuring a detailed kinodynamic model for the robot--is compared with acceleration-velocity obstacles [19] and generalised velocity obstacles [20] to showcase its gain in performance. **Wrap up.