Nowadays, there is increasing interest in the development of teamwork skills in the educational context. This growing interest is motivated by its pedagogical effectiveness and the fact that, in labour contexts, enterprises organize their employees in teams to carry out complex projects. Despite its crucial importance in the classroom and industry, there is a lack of support for the team formation process. Not only do many factors influence team performance, but the problem becomes exponentially costly if teams are to be optimized. In this article, we propose a tool whose aim it is to cover such a gap. It combines artificial intelligence techniques such as coalition structure generation, Bayesian learning, and Belbin's role theory to facilitate the generation of working groups in an educational context. This tool improves current state of the art proposals in three ways: i) it takes into account the feedback of other teammates in order to establish the most predominant role of a student instead of self-perception questionnaires; ii) it handles uncertainty with regard to each student's predominant team role; iii) it is iterative since it considers information from several interactions in order to improve the estimation of role assignments. We tested the performance of the proposed tool in an experiment involving students that took part in three different team activities. The experiments suggest that the proposed tool is able to improve different teamwork aspects such as team dynamics and student satisfaction.

Users are not often aware of privacy risks and disclose information in online social networks. They do not consider the audience that will have access to it or the risk that the information continues to spread and may reach an unexpected audience. Moreover, not all users have the same perception of risk. To overcome these issues, we propose a Privacy Risk Score (PRS) that: (1) estimates the reachability of an user's sharing action based on the distance between the user and the potential audience; (2) is described in levels to adjust to the risk perception of individuals; (3) does not require the explicit interaction of individuals since it considers information flows; and (4) can be approximated by centrality metrics for scenarios where there is no access to data about information flows. In this case, if there is access to the network structure, the results show that global metrics such as closeness have a high degree of correlation with PRS. Otherwise, local and social centrality metrics based on ego-networks provide a suitable approximation to PRS. The results in real social networks confirm that local and social centrality metrics based on degree perform well in estimating the privacy risk of users.

Privacy Risk in Online Social Networks (OSNs) is one of the main concerns that has increased in the last few years. Even though social network applications provide mechanisms to control risk, teenagers are not often aware of the privacy risks of disclosing information in online social networks. The privacy decision-making process is complex and users often do not have full knowledge and enough time to evaluate all potential scenarios. They do not consider the audience that will have access to disclosed information or the risk if the information continues to spread and reaches an unexpected audience. To deal with these issues, we propose two soft-paternalism mechanisms that provide information to the user about the privacy risk of publishing information on a social network. That privacy risk is based on a complex privacy metric. To evaluate the mechanisms, we performed an experiment with 42 teenagers. The proposed mechanisms were included in a social network called Pesedia. The results show that there are significant differences in teenagers' behaviors towards better privacy practices when the mechanisms are included in the network.

In the last few years, many researchers have focused on testing the performance of Multiagent Platforms. Results obtained show a lack of performance and scalability on current Multiagent Platforms, but the existing research does not tackle poor efficiency causes. This article is aimed not only at testing the performance of Multiagent Platforms but also the discovery of Multiagent Platform design decisions that can lead to these deficiencies. Therefore, we are able to understand to what extent the internal design of a Multiagent Platform affects its performance. The experiments performed are focused on the features involved in agent communication.

A new generation of open and dynamic systems requires execution frameworks that are capable of being efficient and scalable when large populations of agents are launched. These frameworks must provide efficient support for systems of this kind, by means of an efficient messaging service, agent group management, security issues, etc. To cope with these requirements, in this paper, we present a novel Multiagent Platform that has been developed at the Operating System level. This feature provides high efficiency rates and scalability compared to other high-performance middleware-based Multiagent Platforms.

Nowadays, most multiagent platforms are internally designed as middleware and are usually implemented in Java and run on top of an operating system. This kind of design maximizes portability and reduces the development cost; however, it may lead to low performance and scalability. In this context, our research has the long-term goal of integrating into the operating system some key services which are currently supported by middleware platforms. The first step in achieving this goal is to study some well-known, open-source platforms in order to understand to what extent the internal design of a platform influences its performance.

Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts' scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures.

Security is becoming a major concern in multiagent systems, since an agent's incorrect or inappropriate behaviour may cause non‐desired effects, such as money and data loss. Some multiagent platforms (MAP) are now providing baseline security features, such as authentication, authorization, integrity and confidentiality. However, they fail to support other features related to the sociability skills of agents such as agent groups. What is more, none of the listed MAPs provide a mechanism for preserving the privacy of the users (regarding their identities) that run their agents on such MAPs. In this paper, we present the security infrastructure (SI) of the Magentix MAP, which supports agent groups and preserves user identity privacy. The SI is based on identities that are assigned to all the different entities found in Magentix (users, agents and agent groups). We also provide an evaluation of the SI describing an example application built on top of Magentix and a performance evaluation of it. Copyright © 2010 John Wiley & Sons, Ltd.