Elena del Val

Enhancing decentralized service discovery in open service-oriented multi-agent systems

Service-oriented multi-agent systems are dynamic systems that are populated by heterogeneous agents. These agents model their functionality as services in order to allow heterogeneous agents or other entities to interact with each other in a standardized way. Furthermore, due to the large-scale and adaptative needs of the system, traditional directory facilitators or middle-agents are not suitable for the management of agent services. This article proposes the introduction of homophily in service-oriented multi-agent systems to create efficient decentralized and self-organized structures where agents have a greater probability of establishing links with similar agents than with dissimilar ones. This similarity is based on two social dimensions: the set of services that an agent provides and the organizational roles that it plays. A second contribution is an algorithm for service discovery that it is carried out taking into account the local information that is related to the homophily between agents. The experiments compare our proposal with other proposals in distributed environments. The results show that the proposed structure and algorithm offer desirable features for service discovery in decentralized environments. Specifically, these features provide short paths and a high success rate in the service discovery process and resilience under deliberate failures.

Does the type of event influence how user interactions evolve on Twitter

The number of people using on-line social networks as a new way of communication is continually increasing. The messages that a user writes in these networks and his/her interactions with other users leave a digital trace that is recorded. Thanks to this fact and the use of network theory, the analysis of messages, user interactions, and the complex structures that emerge is greatly facilitated. In addition, information generated in on-line social networks is labeled temporarily, which makes it possible to go a step further analyzing the dynamics of the interaction patterns. In this article, we present an analysis of the evolution of user interactions that take place in television, socio-political, conference, and keynote events on Twitter. Interactions have been modeled as networks that are annotated with the time markers. We study changes in the structural properties at both the network level and the node level. As a result of this analysis, we have detected patterns of network evolution and common structural features as well as differences among the events.

An artificial intelligence tool for heterogeneous team formation in the classroom

Abstract 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 organise 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 optimised. 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.

A formal model based on Game Theory for the analysis of cooperation in distributed service discovery

New systems can be designed, developed, and managed as societies of agents that interact with each other by offering and providing services. These systems can be viewed as complex networks where nodes are bounded rational agents. In order to deal with complex goals, they require cooperation of the other agents to be able to locate the required services. The aim of this paper is formally and empirically analyze under which circumstances cooperation emerges in decentralized search of services. We propose a repeated game model that formalizes the interactions among agents in a search process where agents are free to choose between cooperate or not in the process. Agents make decisions based on the cost of their actions and the expected reward if they participate forwarding queries in a search process that ends successfully. We propose a strategy that is based on random-walks, and we study under what conditions the strategy is a Nash equilibrium. We performed several experiments in order to evaluate the model and the strategy and to analyze which network structures are more appropriate to promote cooperation.

Analyzing users' activity in online social networks over time through a multi-agent framework

The number of people and organizations using online social networks as a new way of communication is continually increasing. Messages that users write in networks and their interactions with other users leave a digital trace that is recorded. In order to understand what is going on in these virtual environments, it is necessary systems that collect, process, and analyze the information generated. The majority of existing tools analyze information related to an online event once it has finished or in a specific point of time (i.e., without considering an in-depth analysis of the evolution of users’ activity during the event). They focus on an analysis based on statistics about the quantity of information generated in an event. In this article, we present a multi-agent system that automates the process of gathering data from users’ activity in social networks and performs an in-depth analysis of the evolution of social behavior at different levels of granularity in online events based on network theory metrics. We evaluated its functionality analyzing users’ activity in events on Twitter.

Simulating a Collective Intelligence Approach to Student Team Formation

Teamwork is now a critical competence in the higher education area, and it has become a critical task in educational and management environments. Unfortunately, looking for optimal or near optimal teams is a costly task for humans due to the exponential number of outcomes. For this reason, in this paper we present a computer-aided policy that facilitates the automatic generation of near optimal teams based on collective intelligence, coalition structure generation, and Bayesian learning. We carried out simulations in hypothetic classroom scenarios that show that the policy is capable of converging towards the optimal solution as long as students do not have great difficulties evaluating others.

U-Tool: A Urban-Toolkit for Enhancing City Maps Through Citizens’ Activity

The study of the dynamics of cities has become a topic of particular relevance when planning the urban development or analyzing their influence on citizen activities and how citizens interact with the cities. The availability of updated data in real-time about what is happening in a city is of vital importance for the development of what is known as smart cities.

A General Framework for Testing Different Student Team Formation Strategies

One of the most important problems faced by teachers is grouping students into proper teams. The task is complex, as many technical and interpersonal factors could affect team dynamics, with no clear indication of which factors may be more relevant. Not only the problem is conceptually complex, but its computational complexity is also exponential, which precludes teachers from optimally applying strategies by hand. The tool presented in this paper aims to cover both gaps: first, it provides a range of grouping strategies for testing, and second, it provides artificial intelligence mechanisms that in practice tone down the computational cost of the problem.

Utility-Based Mechanism for Structural Self-Organization in Service-Oriented MAS

Structural relations established among agents influence the performance of decentralized service discovery process in multiagent systems. Moreover, distributed systems should be able to adapt their structural relations to changes in environmental conditions. In this article, we present a service-oriented multiagent systems, where agents initially self-organize their structural relations based on the similarity of their services. During the service discovery process, agents integrate a mechanism that facilitates the self-organization of their structural relations to adapt the structure of the system to the service demand. This mechanism facilitates the task of decentralized service discovery and improves its performance. Each agent has local knowledge about its direct neighbors and the queries received during discovery processes. With this information, an agent is able to analyze its structural relations and decide when it is more appropriate to modify its direct neighbors and select the most suitable acquaintances to replace them. The experimental evaluation shows how this self-organization mechanism improves the overall performance of the service discovery process in the system when the service demand changes.

Composing and Ensuring Time-Bounded Agent Services

There are situations where an agent needs to compose several services together to achieve its goals. Moreover, if these goals should be fulfilled before a deadline, the problem of service composition become more complex. In this paper a multi-agent framework is presented to deal with service composition considering service execution time taking into account the availability and the workload of the agent that offers the service.