[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.

Assessing patient risk or degree of vulnerability is crucial for effective nursing care, resource allocation and prevention of complications and adverse events. A scale-based system evaluates hospitalised patients, capturing their status across domains by aggregating and weighting scale scores. Currently, in most hospitals, nurses manually interpret these scores and record the data. Even in high-tech hospitals with electronic health records (EHRs) the interpretation of results still depends on manual analysis, leading to subjectivity and errors. The aim of this project is to develop a predictive system that is seamlessly integrated into the EHR as a decision support tool. The system will categorise patients based on their need for resource allocation, which is interpreted as risk. Real data from 1800 patients were analysed and showed 86% of accuracy in predicting risk using a decision tree algorithm.

In recent years, there has been a strong desire to meet the challenge of electrification of vehicles in order to achieve the decarbonization objective. However, as sales of electric vehicles have increased, there is a significant lack of infrastructure to support the charging of this type of vehicle. The infrastructural deficiencies are even more evident in the interurban environment, where the autonomy in kilometers of the battery is a critical issue. To minimize the substantial economic costs involved in installing sufficient charging points to ensure any interurban journey, it is necessary to establish mechanisms that evaluate appropriate locations to deploy the necessary stations. Accordingly, this paper proposes using an evolutionary approach to calculate the most suitable locations in an interurban environment for electric charging stations. For this purpose, different input information is taken into account in the allocation process. The proposed algorithm has been tested using real data from the USA. The results assess the current infrastructure and show the advantages of the locations proposed by the algorithm.

One of the main challenges in affective computing is the development of models to represent the information that is inherent to emotions. It is necessary to consider that the terms used by humans to name emotions depend on the culture and language used. This article presents an experiment-based method to represent and adapt emotion terms to different cultural environments. We propose using circular boxplots to analyze the distribution of emotions in the Pleasure-Arousal space. From the results of this analysis, we define a new cross-cultural representation model of emotions in which each emotion term is assigned to an area in the Pleasure-Arousal space. An emotion is represented by a vector in which the direction indicates the type, and the module indicates the intensity of the emotion. We propose two methods based on fuzzy logic to represent and express emotions: the emotion representation process in which the term associated with the recognized emotion is defuzzified and projected as a vector in the Pleasure-Arousal space; and the emotion expression process in which a fuzzification of the vector is produced, generating a fuzzy emotion term that is adapted to the culture and language in which the emotion will be used.

Argumentation schemes are generalised patterns that provide a way to (partially) dissociate the content from the reasoning structure of the argument. On the other hand, Cialdini’s principles of persuasion provide a generic model to analyse the persuasive properties of human interaction (e.g., natural language). Establishing the relationship between principles of persuasion and argumentation schemes can contribute to the improvement of the argument-based human-computer interaction paradigm. In this work, we perform a qualitative analysis of the persuasive properties of argumentation schemes. For that purpose, we present a new study conducted on a population of over one hundred participants, where twelve different argumentation schemes are instanced into four different topics of discussion considering both stances (i.e., in favour and against). Participants are asked to relate these argumentation schemes with the perceived Cialdini’s principles of persuasion. From the results of our study, it is possible to conclude that some of the most commonly used patterns of reasoning in human communication have an underlying persuasive focus, regardless of how they are instanced in natural language argumentation (i.e., their stance, the domain, or their content).

Human social relationships imply conforming to the norms, behaviors, and cultural values of the society, but also socialization of emotions, to learn how to interpret and show them. In multiagent systems, much progress has been made in the analysis and interpretation of both emotions and norms. Nonetheless, the relationship between emotions and norms has hardly been considered and most normative agents do not consider emotions, or vice-versa. In this article, we provide an overview of relevant aspects within the area of normative agents and emotional agents. First we focus on the concept of norm, the different types of norms, its life cycle and a review of multiagent normative systems. Second, we present the most relevant theories of emotions, the life cycle of an agent’s emotions, and how emotions have been included through computational models in multiagent systems. Next, we present an analysis of proposals that integrate emotions and norms in multiagent systems. From this analysis, four relationships are detected between norms and emotions, which we analyze in detail and discuss how these relationships have been tackled in the reviewed proposals. Finally, we present a proposal for an abstract architecture of a Normative Emotional Agent that covers these four norm-emotion relationships.

The deployment of a charging infrastructure to cover the increasing demand of electric vehicles (EVs) has become a crucial problem in smart cities. Additionally, the penetration of the EV will increase once the users can have enough charging stations. In this work, we tackle the problem of locating a set of charging stations in a smart city considering heterogeneous data sources such as open data city portals, geo-located social network data, and energy transformer substations. We use a multi-objective genetic algorithm to optimize the charging station locations by maximizing the utility and minimizing the cost. Our proposal is validated through a case study and several experimental results.

If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity . Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of “platform as a service” or “software as a service”, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers , dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing , analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.

Affective characteristics are crucial factors that influence human behavior, and often, the prevalence of either emotions or reason varies on each individual. We aim to facilitate the development of agents’ reasoning considering their affective characteristics. We first identify core processes in an affective BDI agent, and we integrate them into an affective agent architecture ( GenIA 3 ). These tasks include the extension of the BDI agent reasoning cycle to be compliant with the architecture, the extension of the agent language (Jason) to support affect-based reasoning, and the adjustment of the equilibrium between the agent’s affective and rational sides.

In distributed environments where entities only have a partial view of the system, cooperation is a key issue. In the case of decentralized service discovery in open agent societies, agents only know about the services they provide and who their direct neighbors are. Therefore, they need the cooperation of their neighbors in order to locate the required services. However, cooperation is not always present in open systems. Noncooperative agents pursuing their own goals could refuse to forward queries from other agents to avoid the cost of this action; therefore, the efficiency of the decentralized service discovery could be seriously damaged. In this article, we propose the combination of incentives and local structural changes in order to promote cooperation in the service discovery process. The results show that, even in scenarios where the predominant behavior is not collaborative, cooperation emerges.

Open Multi-Agent Systems (MAS) are formed by autonomous and heterogeneous agents that may pursue different and even conflicting goals. Therefore, no assumption can be a priori made about their behaviours. Thus, it is needed to constrain the potential excesses of agent autonomous behaviour by means of regulation of agent interaction. Because of this, ‘social’ notions, such as organizations, institutions and norms, have been introduced in the MAS research as coordination and cooperation mechanisms. However, there is still a need to define frameworks which take these organizational abstractions into account. With this aim, this work describes a model of norms for agent organizations that covers both institutional and interaction levels, as well as different types of norms (i.e. constitutive, regulative and procedural norms). This model of norms has been implemented in a specific multi-agent platform, named THOMAS (Methods, Techniques and Tools for Open Multi-Agent Systems), which provides a suitable framework for the management of agent organizations.

In this article, an agent-based negotiation model for negotiation teams that negotiate a deal with an opponent is presented. Agent-based negotiation teams are groups of agents that join together as a single negotiation party because they share an interest that is related to the negotiation process. The model relies on a trusted mediator that coordinates and helps team members in the decisions that they have to take during the negotiation process: which offer is sent to the opponent, and whether the offers received from the opponent are accepted. The main strength of the proposed negotiation model is the fact that it guarantees unanimity within team decisions since decisions report a utility to team members that is greater than or equal to their aspiration levels at each negotiation round. This work analyzes how unanimous decisions are taken within the team and the robustness of the model against different types of manipulations. An empirical evaluation is also performed to study the impact of the different parameters of the model.

Many websites nowadays allow social networking between their users in an explicit or implicit way. In this work, we show how the theory of argumentation schemes can provide a valuable help to formalize and structure on-line discussions and user opinions in decision support and business oriented websites that hold social networks among their users. A real study case is considered and analysed. Then, guidelines for website and system design are provided to enhance social decision support and recommendations with argumentation.

The shift from made-to-stock to made-to-order has resulted in new manufacturing environments that require IT frameworks able to support this new dynamism. Collaborative manufacturing environments demonstrate considerable potential in responding to this need. As such, open manufacturing approaches are becoming more and more the appropriate tool and technology to meet the high expectations of their customers, who, in today's economy, demand absolutely the best service, price, delivery time and product quality. In this paper an agent-supported infrastructure for e-manufacturing is proposed as the enabling tool for implementing virtual collaborative manufacturing environments. In such an environment an open manufacturing process is seeing as a virtual scenario in which different manufacturing services are choreographed and orchestrated in order to get a product by different autonomous entities from different manufacturing systems.

Computer game development covers many different areas, such as graphics, artificial intelligence (AI) and network communications. Nowadays, players demand more sophisticated and credible computer-controlled opponents, but results are sometimes unsatisfactory. This is due to the need of real-time processing constraints to reach an acceptable feeling of immersion for the user. The development of systems, tools and development environments can play an important role in the progress of this field. The combination of AI techniques and virtual reality (or virtual environments) has given birth to the field of intelligent virtual environments (IVEs). An IVE is a virtual environment simulating a physical (or real) world, inhabited by autonomous intelligent entities [Luck and Aylett 2000]. These entities have to interact in / with the virtual environment as if they were real entities in the real world. There are some other typical problems of an IVE that we have to solve, as explained in [Bierbaum et al. 2001]: independence of the underlying technologies, user interactions (not only displaying images, but using specific devices such as trackers, gloves), synchronization of both data and image (even in case of multiple displays).

The application of simulation to manufacturing has contributed to a generation of tools that is focused on specific problem solving related to automation, planning, material flow, supply chain, logistics, etc. Nowadays, manufacturing has evolved and its needs have changed. After an analysis of the current manufacturing simulation tools, we propose an approach based on multiagent systems theory, which covers current manufacturing needs.