[EN] Team formation is essential for developing teamwork-related skills in educational settings. The problem of team formation in the classroom consists of partitioning a classroom into non-overlapping teams of students, including every single student. Several algorithms have been proposed to automate the formation of teams, each employing different criteria for guiding the team formation process. Traditionally, metaheuristics have been a common approach due to the combinatorial complexity of the problem. This paper introduces a novel and general evolutionary algorithm for team formation in the classroom guided by mutation, the general concept of synergy between team members, and local search. Our algorithm allows for flexible team size constraints and the inclusion of compulsory and forbidden student combinations, which are not considered in existing methods but are important for capturing human relationships in the classroom. In addition, our algorithm is independent of the specific objective function employed to evaluate the teams formed. We present experiments comparing our proposal with other state-of-the-art algorithms, demonstrating robust performance across different objective functions employed in the team formation literature, superior scalability as the problem size increases, and remarkable performance in settings with or without the aforementioned constraints.

Abstract Throughout recent years, several researchers have proposed computational tools and algorithms to support team formation in the classroom. The result is that team formation algorithms have been widely applied in classroom environments to create well-balanced teams. One of the challenges in designing algorithms for automatic team formation is designing an appropriate function to estimate team performance, which is used as part of the optimization algorithm that divides students into teams. This function (referred to as a team evaluation heuristic) serves as an approximation to team performance, which is a complex phenomenon that is difficult to quantitatively assess in many settings and that cannot be accurately calculated prior to the task at hand. Despite showing their relative success compared to traditional and manual team formation strategies (manually employed by lecturers and teachers), there is a lack of research comparing team evaluation heuristics in a real classroom setting. Such a comparison would help teachers, practitioners, and system designers to appropriately select the most suitable team formation algorithms. In this article, we present an experimental evaluation that was carried out in a Bachelor’s Degree Program in Tourism that compares two team evaluation heuristics based on Belbin and Myer-Briggs. The experimental evaluation was carried out by means of an intelligent, extensible team formation tool whose optimization is based on an integer linear model that can be extended to support different team evaluation heuristics.

Special issue of Teams in Multiagent Systems

The next generation of smart grid technologies demands intelligent capabilities for communication, interaction, monitoring, storage, and energy transmission. Multi-agent systems are envisioned to provide autonomic and adaptability features to these systems in order to gain advantage in their current environments. In this article we present a mechanism for providing distributed energy storage systems (DESSs) with intelligent capabilities. In more detail, we propose a self-configurable mechanism that allows a DESS to adapt itself according to the future environmental requirements. This mechanism is aimed at reducing the costs at which energy is purchased from the market.

Organization adaptation requires determining the consequences of applying changes not only in terms of the benefits provided but also measuring the adaptation costs as well as the impact that these changes have on all of the components of the organization. In this paper, we provide an approach for adaptation in multiagent systems based on a multidimensional transition deliberation mechanism (MTDM). This approach considers transitions in multiple dimensions and is aimed at obtaining the adaptation with the highest potential for improvement in utility based on the costs of adaptation. The approach provides an accurate measurement of the impact of the adaptation since it determines the organization that is to be transitioned to as well as the changes required to carry out this transition. We show an example of adaptation in a service provider network environment in order to demonstrate that the measurement of the adaptation consequences taken by the MTDM improves the organization performance more than the other approaches.

Abstract 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.