Search

Artificial Neural Networks (NNs) have been recognized as a powerful tool for automatically learning complex relationships in data. In this paper, we propose to apply such a tool for modeling forest regeneration, a possibility not yet investigated in the literature. In order to evaluate the capability of NNs to simulate initial recruitment of pine species in Mediterranean forests, a feed-forward multi-layer neural network has been applied to seed germination and seedling survival of four pine species under three soil conditions, with or without seed protection, in Castilla La Mancha (Central-Eastern Spain). The experimental campaign has shown good performance in predicting the two pine initial recruitment stages. The proposed approach may help to predict the success of natural regeneration in Mediterranean pine forests under different basal areas and management strategies.

The Internet of Things (IoT) promises to change many aspects of our daily lives thanks to the opportunity of interconnecting a massive numbers of smart objects with increasing computational, storage, communication, and power capabilities, in such a way making “smart” and “interactive” most of the world around us. In other words, smart objects and humans will be involved together in pervasive, proactive, and collaborative activities to orchestrate and execute increasingly complex and sophisticated tasks. In such a scenario, thanks also to a greater ecological awareness of people, a promising field of application for IoT technology is the monitoring of natural habitats in automatic or semiautomatic mode. A potentially effective and efficient solution is to form composite teams joining human operators and IoT devices. In such teams, not only the kind of team members will be different, i.e., humans and IoT devices, but also the IoT devices will be heterogeneous among them in terms of characteristics and performance. However, a basic requirement for a good team is the existence of high levels of mutual trust among its members. In other words, for the formation of a good team, it becomes of primary relevance to know and adequately represent the trustworthiness of the individual team members. To this end, our contribution can be summarized as (i) introducing a trust measure that takes into account both the reputation of devices and the accuracy of their measures; (ii) designing a framework that based on the proposed trust measure forms temporary teams for environmental ecosystems made of humans and IoT devices; and (iii) testing the proposed framework by simulating a collaborative environmental monitoring activity. The simulation results confirmed the advantages of the proposed approach in terms of performance and appreciation of the composite temporary teams that have been formed in this way.

In the Internet of Things, smart objects can build multidimensional and context-sensitive network infrastructures potentially rich of social interactions. Smart objects can be associated with software agents to boost social interactions and realizing complex and sophisticated forms of collaboration of objects with both other objects and people. In such a scenario, there exists the possibility to interact with unreliable partners exposing agents to the risks deriving by malicious behaviors. To mitigate these risks, Trust and Reputation Systems can be adopted to provide each agent with appropriate trustworthiness measures about the potential counterparts in order to select the best ones. In this context, our contribution consists of (i) a method to preliminarily identify the best candidates as malicious in order to consider them as pre-untrusted entities and (ii) a novel effective reputation model able to detect collusive malicious agents without introducing collateral effects with respect to the reputation scores of honest agents.

Smart Workshops are experiencing the need of a mobile intelligence for mining both learning patterns and knowledge from the wide sea of data generated by both mobile users and mobile technologies. Indeed, mobile intelligence would represent the ideal substratum for providing "agentified" robots with a plethora of advanced capabilities (e.g., visual recognition, fault detection, self-recovery) and, hence, with high-level functionalities, like production line control, asset movement, connectivity restore. Besides the operational plane, however, mobile intelligence can be successfully exploited also in organizational tasks, like the formation of temporary, ad-hoc teams for accomplishing a given target. The complexity of some industrial operations, indeed, often demands the involvement of several, heterogeneous group of robots and the adequate representation of the reciprocal trustworthiness represents a key pre-requisite. It holds particularly for the Automated Guided Vehicles (AGVs) which are increasingly involved in collaborative activities aimed to optimise storage, picking, and transport functions in a wide variety of workshop areas. Therefore, in this paper we define a trustworthiness model for agentified AGVs based on the mix of their reputation and reliability and we present an agent-based framework implementing the related team formation strategy. The improvements obtained in terms of effectiveness and efficiency from the AGV team are observed and measured through a simulation activity, in which realistic settings for an industrial applications have been considered.

The need of managing complex and interactive activities is becoming a key challenge in the “Internet of Things” (IoT) and leads to request large hardware and power resources. A possibility of facing such a problem is represented by the possibility of virtualizing physical IoT environments over the so called Cloud-of-Things (CoT), where each device is associated with one or more software agents working in the Cloud on its behalf. In this open and heterogeneous context, IoT devices obtain significant advantages by the social cooperation of software agents, and the selection of the most trustworthy partners for cooperating becomes a crucial issue, making necessary to use a suitable trust model. The cooperation activity can be further improved by clustering agents in different groups on the basis of trust measures, allowing each agent will to interact with the agents belonging to its own group. To this purpose, we designed an algorithm to form agent groups on the basis of information about reliability and reputation collected by the agents. In order to validate both the efficiency and effectiveness of our approach, we performed some experiments in a simulated scenario, which showed significant advantages introduces by the use of the trust measures.

Observing the features of the information actually stored in the Web, we can recognize that an important issue to be investigated is that of discovering relationships between groups of objects. In particular, a great interest is emerging on finding groups of objects mutually linked by reciprocal relationships of trustworthiness. In this paper, we propose a model to represent the case of trust-based groups of objects, and we present an algorithm for detecting trust associations in virtual communities in presence of these groups. Such an algorithm consists in determining particular sub-structures of the community, called trust groups, representing objects mutually connected by strong trust relationships. We technically formalize our idea and algorithm, and we present a complete example of how our approach works.

Members of virtual communities generally expect that their groups satisfy some given requirements. For this aim, the profile matching between user requirements and group characteristics can be considered as the most natural way to represent the group homogeneity, measuring how much the group members are mutually linked. However, optimizing profile matching does not guarantee that the group will continue to be homogeneous in time (i.e., cohesive). In the past we have already shown that, when group formation is driven by trust measures and profile matching group homogeneity is improved. In this work, we prove by experiments on a dataset extracted from a real social network, that trust measures can be used to effectively replace profile matching for optimizing group’s cohesion. Furthermore, we prove also that using a local trust measure will does not penalize the cohesion of the group.

The use of network technology to provide online courses is the latest trend in the training and development industry and has been defined as the “e-Learning revolution”. On the other hand, Online Social Networks (OSNs) represent today an effective possibility to have common and easy-to-use platforms for supporting e-Learning activities. However, as underlined by previous studies, many of the proposed e-Learning systems can result in confusion and decrease the learner’s interest. In this paper, we introduce the possibility to form e-Learning classes in the context of OSNs. At the best of our knowledge, any of the approaches proposed in the past considers the evolution of on-line classes as a problem of matching the individual users’ profiles with the profiles of the classes. In this paper, we propose an algorithm that exploits a multi-agent system to suitably distribute such a matching computation on all the user devices. The good effectiveness and the promising effciency of our approach is shown by the experimental results obtained on simulated On-line Social Networks data.

Improvements in computational and network capabilities of mobile devices along with the wide spread of wireless networks for smart cities, allow users to make accessible temporarily unused communication resources for free or for a fee. Such interesting scenario introduces, however, some critical issues due to reliability of users sharing/consuming resources, which are a further concern with respect to those, strictly referred to the security. To tackle such problems, in this paper we propose a distributed multi-agent framework, exploiting reputation information, which considers Both the price paid by mobile users for resources and some countermeasures to detect malicious users, as confirmed by experimental results.

In this work we deal with the issue of optimizing the global Quality of Service (QoS) of a Grid Federation by means of an aggregation model specifically designed for intelligent agents assisting Grid nodes. The proposed model relies on an algorithm, called FGF (Friendship and Group Formation), by which the nodes select their partners with the aim of maximizing the QoS they perceive when a computational task requires the collaboration of several Grid nodes. In the proposed solution, in order to assist the selection of the partners, a suitable trust model has been designed. Since jobs sent to Grid Federations hold complex requirements involving well defined resource sets, trust values are calculated for specific sets of resources. We also provide a theoretical foundation and some experiments to prove that, by means of the adoption of the FGF algorithm suitably supported by the proposed trust model, the Grid Capital (which respect the global QoS) of the Grid Federation is eventually improved.

Online Social Networks are suitable environments for e-Learning for several reasons. First of all,there are many similarities between social network groups and classrooms. Furthermore,trust relationships taking place within groups can be exploited to give to the users the needed motivations to be engaged in classroom activities. In this paper we exploit information about users’ skills,interactions and trust relationships,which are supposed to be available on Online Social Networks,to design a model for managing formation and evolution of e-Learning classes and providing suggestions to a user about the best class to join with and to the class itself about the best students to accept. The proposed approach is validated by a simulation which proves the convergence of the distributed algorithm discussed in this paper.