From computer games to human societies, many natural and artificial phenomena can be represented as multi-agent systems. Over time, these systems have been proven a really powerful tool for modelling and understanding phenomena in fields, such as economics and trading, health care, urban planning and social sciences. However, although, intelligent agents have been around for years, their actual implementation is still in its early stages. Since the late nineties many agent platforms have been developed. Some of them have already been abandoned whereas others continue releasing new versions. On the other hand, the agent-oriented research community is still providing more and more new platforms. This vast amount of platform options leads to a high degree of heterogeneity. Hence, a common problem is how people interested in using multi-agent systems should choose which platform to use in order to benefit from agent technology. This decision was usually left to word of mouth, past experiences or platform publicity, lately however people depend on solid survey articles. To date, in most cases multi-agent system surveys describe only the basic characteristics of a few representatives without even providing any classification of the systems themselves. This article presents a comparative up-to-date review of the most promising existing agent platforms that can be used. It is based on universal comparison and evaluation criteria, proposing classifications for helping readers to understand which agent platforms broadly exhibit similar properties and in which situations which choices should be made.

Today, the Internet of Things, Big Data and Machine Learning are a regular part of people's daily lives, especially for the elderly or the so-called Silver Economy. There are more and more applications in which collecting and storing information about users (and their context) to produce classifications and predictions allows the optimisation of the services offered, improving the life quality of the citizens. In this sense, leisure cruises represent environments where it is necessary to provide quality services for a reasonable price. This requires understandings the behaviour of users both locally (i.e. each cruise) and globally (i.e. all cruises). In this respect, IoT, Big Data and Machine Learning are presented as ideal technologies for prediction-making and the improvement of the quality of services. However, on ships at sea the transfer rate of the Internet connection is limited. In this sense, Edge Computing allows taking part in the application computing to the Edge of the network, closer to the IoT layer. Data can be filtered and pre-processed in the Edge before it is transmitted to the Cloud, reducing its volume and the associated costs of its computing and storage in the Cloud. Machine Learning algorithms can also be applied to recognise patterns and anomalies on the Edge itself, even if the connectivity with the Cloud is lost. In this paper, we propose the design of a platform which aims to improve the quality of life of the elderly on leisure cruises.