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The introduction of the IoT technology and its pervasive penetration in our daily life implies that IoT smart objects can often participate in social events, and in this case their paradigm of interaction is commonly denoted as social IoT (SIoT). To make reliable transactions into a SIoT scenario, in this article, we introduce a multi-agent SIoT architecture, which integrates a reputation system based on a clustering of the SOs. In our framework, when a SO looks for a resource and detects a reliable partner having that resource, then the two SOs can interact to make a transaction, and at the end of the transaction each of them provides a feedback about the partner to the local reputation system of the edge server, to update the SOs' reputation scores. Moreover, each edge server sends these reputation scores to the cloud, which updates the associated reputation values of the SOs, deriving from experiences coming from all the edge domains. This architecture provides a given object, moving from an edge domain to another one, with the possibility to have an updated value of its reputation, represented by the value stored in the cloud. We have validated our approach by a campaign of simulations, whose results seem particularly promising.

In competitive Multi-Agent Systems (MASs) for IoT, an important task is to form friendships and groups for mutual cooperation. The existing proposals try to improve the profit of individual agent or group but, on the other hand, the most aggressive agents may take advantages from these approaches by means of malicious behaviors. In this work we model a MAS framework of non cooperative games by taking into account i) the reliability of agents relationships through a trust model and ii) the community ability to furnish a good environment to its members through its social capital. Thus, we designed an algorithm that maximizes the social capital. We emphasize two main findings: i) the result is a Nash equilibrium and ii) the agents are awarded only if they behave correctly.

An important issue in Online Social Networks consists of the capability to generate useful recommen- dations for users, as peers to contact in order to establish friendships and collaborations, interesting resources to use and so on. This implies the necessity of evaluating the trustworthiness a user should assign to other members of his/her online community. In the past literature, a common approach for predicting trust is represented by a number of models that rely on “global” reputation: they are based on the evaluation of the behaviors of the users, that is shared across the entire community. These models, however, show an evident limitation due to the difficulty of taking the effects of malicious or fraudulent behaviors into account, thus making the feedback themselves. Other approaches, that consider also a local perspective of the trust, are limited by the fact they are supervised, i.e. they need a training phase in generating recommendations. In this paper, we propose a novel approach to extend global reputation models with a local reputation, computed on the ego-network of the user, by means of an unsupervised approach. We characterize our model by considering (i) the different relevance given to local and global repu- tation, (ii) the threshold that is used to consider a user unreliable and (iii) the dimension of the user’s ego-network. Experiments performed on a real data set show that global reputation is useful only if the size of the user ego-network is small, as in the case of a newcomer. Moreover, the combined usage of global and local reputation leads to predict the expected trust with a high level of precision.

Thematic groups are gaining a lot of attention and high centrality in OSNs, as users share opinions and/or mutually collaborate for reaching their targets. Since users can be affiliated with groups belonging to different social networks, they can be supported by personal software agents able to perform activities aimed at supporting relationships and mutual cooperation among heterogeneous OSNs thematic groups. Basically software agents can encode users profiles with detailed information to be related with specific groups. This work discusses a multi-agent framework whose structure focuses on the role of software agents and the use of a common, shared dictionary for each group. Personal software agents are associated to OSN users to share knowledge for a specific theme for a number of groups related to that topic. Group agents are defined to support each group on each OSN by interacting with personal agents to manage group affiliation and enrich the common dictionaries of their own groups. The common dictionary of the group is a key element to provide knowledge sharing and interoperability between personal and group agents. In the proposed approach each user agent is able to personalize its own dictionary and enrich that of its own groups by means of selected categories.

Thematic groups are gaining a lot of attention and high centrality in OSNs, as users share opinions and/or mutually collaborate for reaching their targets. Since users can be affiliated with groups belonging to different social networks, they can be supported by personal software agents able to perform activities aimed at supporting relationships and mutual cooperation among heterogeneous OSNs thematic groups. Basically software agents can encode users profiles with detailed information to be related with specific groups. This work discusses a multi-agent framework whose structure focuses on the role of software agents and the use of a common, shared dictionary for each group. Personal software agents are associated to OSN users to share knowledge for a specific theme for a number of groups related to that topic. Group agents are defined to support each group on each OSN by interacting with personal agents to manage group affiliation and enrich the common dictionaries of their own groups. The common dictionary of the group is a key element to provide knowledge sharing and interoperability between personal and group agents. In the proposed approach each user agent is able to personalize its own dictionary and enrich that of its own groups by means of selected categories.

Learning software agents are able to assist Cloud providers in taking decisions about resource management at any level, as they are able to collect knowledge and improve their performances over time by means of learning strategies. On the other hand Cloud Federations allow providers to share computational infrastructures in order to build a distributed, interoperable multi-cloud context. In this work we present an evolutionary approach based on agent cloning, i.e. a mechanism of agent reproduction allowing providers to substitute an “unsatisfactory” agent acting in a “cloud context” with a clone of an existing agent having a suitable knowledge and a good reputation in the multicloud context. By this approach, cloud agents performances can be improved because they are substituted with agent clones that have shown a better behavior.

Cloud Computing is a versatile computing paradigm capable of attracting a wide variety of applications. However, the necessity to provide a wide range of complex services led providers to establish mutual agreements to provide large-scale distributed multi-cloud environments. Providers gain the opportunity to compose service workflows that are effective and efficient, taking resources of their own competitors, and the capability to satisfy unexpected workload peaks. In this paper, we propose a reputation-based model aiming at supporting the service composition by considering measures of QoS collected by the measuring systems, and reputation measures collected with the customers by means of users feedback.

In this work, we propose an algorithm, called E-Learning Ontology Enrichment (ELOE), to derive a global representation from the personal ontologies of different agents present in an e-Learning MAS. Using ELOE, each agent of a MAS-based e-Learning system can autonomously enrich his own ontology by using semantic negotiation and, at the same time, it can access to the global ontology to have a view of the terms used by all the other agents. Each term of the global ontology is associated with a set of meanings, and each meaning is associated with the set of agents that have used it. This way an agent, desiring to send a message to an interlocutor, is able of choosing from the global ontology the most suitable term with the most appropriate meaning. Only if the agent does not find in the global ontology the appropriate term, he will use a personal term that probably will lead to a new semantic negotiation process. This way, the use of the onerous task of the semantic negotiation will be limited to only the strictly necessary situations, and consequently the whole communication cost is decreased.