# Multi-Agent Systems
## 6 MAS in Smart Cities  
The concept of the smart city [30] arises from the need to find a solution to rapid population growth and the risks this entails for a city, economic risks such as unemployment or physical risks such as over-pollution. To solve these problems, different technologies, among many others, have been applied in order to find solutions in this area. Multi-agent systems together with the Internet of Things [18] are traditionally the most employed.  
Typically, these concepts come together, designing intercommunicated networks that responds to the needs of citizens both individually and as a whole and also monitoring through sensors the levels of pollution, traffic, noise, etc. A smart city is a great intercommunicated organism that, together with an intelligent government, seeks to improve the quality of life of its citizens [31].  
Therefore, a smart city would be full of sensors constantly collecting information about actions that happen in the city, humidity sensors, temperature sensors, noise, pollution, etc. All these sensors are part of a data collection system that will be responsible for processing information quickly and intelligently. It is for this point where the use of multi-agent systems make sense. The decentralized control of an MAS offers the possibility of managing all available information in a distributed way and also coordinating possible actions effectively over the city. Moreover, decision making processes, apart from being coordinated, can execute in parallel actions at different points of the city, without a strong centralized control, which gives greater flexibility and adaptation to the whole system.  
This Special Issue contributes with three works in the area of smart cities. The first work [32] proposed a multi-agent system that provides visualization and prediction tools for bike sharing systems. The proposed MAS includes an agent that performs data collection and cleaning processes, and it is also capable of creating demand forecasting models for each bicycle station. The authors included a case study, which validated the proposed system, by implementing it in a public bicycle sharing system in Salamanca, called SalenBici. In the proposed solution, the information collected was employed by the agents who performed demand forecasting. Moreover, different regression algorithms have also been employed in the process of bike demand prediction. Additionally, a statistical analysis has been performed in order to show the differences in their performance and to determine the relevance of results. The second approach presented in this Special Issue regarding smart cities is the work done in [33], where a multi-agent system was proposed, in order to facilitate the analysis of different possible placement configurations for electric vehicles charging stations in a city. The MAS proposed in this paper integrates the information extracted from heterogeneous data sources as a starting point to specify the areas where future charging stations could potentially be placed. To do this, the proposed MAS integrates an optimization algorithm, which is in charge of the locating process. Finally, the third contribution, presented in [34], analyzed the use of agreement technologies [35], which envisions the next-generation of open distributed systems, where interactions between software components are based on the concept of agreement. In this sense, the authors increased the coordination among entities using agreement technologies in the domain of smart cities as a way to enable the development of novel applications. Concretely, they proposed the use of these techniques in a specific domain such as the coordination in emergency medical services, which includes many tasks that require flexible, on-demand negotiation, initiation, coordination, information exchange, and supervision among different involved entities. All these aspects can be solved through the use of, as previously mentioned, agreement technologies.