Abstract In this work, we explore the impact of adaptive fuzzy measures on edge detection, aiming to enhance how computers interpret images by identifying edges more accurately. Traditional methods rely on analysing changes in image brightness to locate edges, but they often use fixed rules that do not account for the unique characteristics of each image. Our approach differs by adjusting fuzzy measures based on the information within specific areas of an image under a sliding window approach, utilizing a variety of fusion functions and generalizations of the Choquet integral to analyse and combine pixel data. The proposed method is flexible, allowing for the adaptation of measures in response to the image's local features. We put our method to the test against the well‐established Canny edge detector to evaluate its effectiveness. Our experimental results suggest that by adapting fuzzy measures for each image section, we can improve edge detection results.

[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.

In the digital era, managing vast quantities of uncategorised text-based documents poses a significant challenge for institutions, as experienced by the Universidad Politécnica de Valencia (UPV) with their internal educational video system. Due to the lack of proper categorisation, the issue is represented by the difficulty students face in finding relevant content and the teachers keeping it updated. The paper aims to propose various Machine Learning and Deep Learning techniques aimed at assigning meaningful topics to these transcriptions, similar to how Wikipedia uses succinct, context-rich labels for its articles. This approach streamlines content access, enhances internal tracking and adds significant value to the institutions by improving data management and user experience.

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.

Abstract The modeling of fleet vehicles as self-interested agents brings a realistic perspective to open fleet transportation research. This feature allows us to model the fleet operation from a non-cooperative point of view. In this work, we study parcel delivery in a city with limited resources (roads and charging stations). We designed and implemented a system composed of a multi-agent planner and a game-theoretic coordination algorithm: a Best-Response Fleet Planner. The system allows for the self-organization of the transportation system by coordinating a fleet of self-interested electric vehicles. The system’s operation is optimized together with resource usage while preserving the agents’ private interests, allowing each agent to plan its actions. The results show that our system has higher scalability than similar approaches, allowing it to function for a considerable number of agents in settings that feature congestion and conflicts. Additionally, overall solution quality is improved compared to other coordination systems, reducing congestion and avoiding unnecessary waiting times.

Abstract Ambient Assisted Living (AAL) and Ambient Intelligence (AmI) try to achieve a future where technology surrounds the users and helps them in their daily lives. In this sense, the urgent need of solutions to cover the rapid increase of the elderly population with chronic diseases led to the increase of projects related with AAL and AmI. During the latest years, several projects have been proposed to tackle different medical problems, some building devices and others services. This paper presents iGenda and its evolution, the UserAccess, with the main objective of developing an AAL platform. It features an analysis of the latest developments and points future directions for the work. These projects display the importance of the interoperability of the platforms, demonstrating a case study for AAL development.

Nowadays, there is a need to provide new applications which allow the definition and implementation of safe environments that attends to the user needs and increases their wellbeing. In this sense, this paper introduces the EJaCalIVE framework which allows the creation of emotional virtual environments that incorporate agents, eHealth related devices, human actors, and emotions projecting them virtually and managing the interaction between all the elements. In this way, the proposed framework allows the design and programming of intelligent virtual environments, as well as the simulation and detection of human emotions which can be used for the improvement of the decision-making processes of the developed entities. The paper also shows a case study that enforces the need of this framework in common environments like nursing homes or assisted living facilities. Concretely, the case study proposes the simulation of a residence for the elderly. The main goal is to have an emotion-based simulation to train an assistance robot avoiding the complexity involved in working with the real elders. The main advantage of the proposed framework is to provide a safe environment, that is, an environment where users are able to interact safely with the system.

Human-agent societies refer to applications where virtual agents and humans coexist and interact transparently into a fully integrated environment. One of the most important aspects in this kind of applications is including emotional states of the agents (humans or not) in the decision-making process. In this sense, this paper presents the applicability of the JaCalIVE (Jason Cartago implemented intelligent virtual environment) framework for developing this kind of society. Specifically, the paper presents an ambient intelligence application where humans are immersed into a system that extracts and analyzes the emotional state of a human group. A social emotional model is employed to try to maximize the welfare of those humans by playing the most appropriate music in every moment.

Abstract Multiagent systems are suitable for providing a framework that allows agents to perform collaborative processes in a social context. Furthermore, argumentation is a natural way of reaching agreements between several parties. However, it is difficult to find infrastructures of argumentation offering support for agent societies and their social context. Offering support for agent societies allows representation of more realistic environments to have argumentation dialogues. We propose an infrastructure to develop and execute argumentative agents in an open multiagent system. It offers tools to develop agents with argumentation capabilities. It also offers support for agent societies and their social context. The infrastructure is publicly available. Also, it has been implemented in an application scenario where argumentative agents try to reach an agreement about the best solution to solve a problem reported to the system.

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.

Conflict is an omnipresent phenomenon in human society. It spans from individual decision-making trade-offs such as deciding what to do next (sleep, eat, work, play), to complex scenarios including politics and business. The social sciences, psychology, economy, and biology study the nature of conflict, its consequences, and strategies to successfully deal with it. Over the last decades computer science has joined those disciplines and studies conflict from a computational perspective. This special issue presents a selection of the best papers presented at the First Workshop of Conflict Resolution in Decision Making (COREDEMA). The workshop focused on computational approaches that tackle conflict in order to provide new insights and explore potential applications. The workshop was jointly hosted with the 12th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS) in Salamanca, Spain, from June 4 to 6, 2013.

The aim of this study is to build a computational model to simulate border crossing. Since the 19th century, many theories have been applied to the study of international migration phenomena. These theories cover sociological, economical, and cultural aspects. All the theories aim at estimating the magnitude of population movements. Multi-Agent Systems (MAS) are presented as a new methodological approach to model and foresee the process of migration. This article discusses how legal aspects can also be treated from a computational point of view. When a migrant reaches the border checkpoint, he/she must deal with the legislation of the destination country, which is applied by the border security police. This interaction at the microlevel between the migrant and the border agent is based on the definition of a behavioral model that includes the migrant's profile, the border officer's profile, and the legislation that rules the border crossing. With this model, the effects at the macrolevel of new policies (which are usually difficult to predict) can be estimated. Changes in legislation or in strictness in applying the existing law at certain borders can influence migration trends; for example, the choice of route. This study presents the main features of the computational model that has been developed to recreate the border-crossing scenario in a Multi-Agent System.

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.

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.

Agent-based negotiation teams are negotiation parties formed by more than a single individual. Individuals unite as a single negotiation party because they share a common goal that is related to a negotiation with one or several opponents. My research goal is providing agent-based computational models for negotiation teams in multi-agent systems.

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.

This work presents a Model Driven Development (MDD) approach to agent-oriented software engineering in order to design and deploy application prototypes in a fast and simple way. This approach is specifically addressed to systems including agents that must be executed on mobile or embedded devices.

Time is an important non-functional parameter to consider in service compositions, especially in environments where a service must be provided before a deadline. This paper presents a framework that deals with service compositions taking into account the service execution time. To enhance this composition it is important to provide service execution times with reliability, taking into account the workload and availability of the service.

This paper presents, a new agent model "specially" designed for the new Android Google SDK, where the Android mobile phone can be considered as a software agent. This agent model has an approach more practical than theoretical because it uses abstractions which makes possible its implementation on d

One of the main critical problems in manufacturing system domains is the production scheduling process, because an agile and reactive production planning and scheduling system is essential in manufacturing. The production scheduling process is a complex problem in which finding a suitable production scheduling can greatly increase the effectiveness of highly flexible production processes. Nevertheless, this high flexibility makes the production scheduling and acquisition of relevant data quite complicated. Therefore, there is a strong demand for a universal and flexible tool for production scheduling capable of increasing the utilization of resources and that supports a decision-making process for the selection of production orders. In this paper, a flexible and adaptive scheduling tool (FAST) to develop an adaptable, fault-tolerant, and scalable scheduling system for a manufacturing environment is presented. This approach is based on multiagent systems (MAS), which provide a natural way to solve problems in domains of this kind.