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Several models have been proposed in the past for representing both reliability and reputation. However, we remark that a crucial point in the practical use of these two measures is represented by the possibility of suitably combining them to support the agent's decision. In the past, we proposed a reliability-reputation model, called RRAF, that allows the user to choose how much importance to give to the reliability with respect to the reputation. However, RRAF shows some limitations, namely: (i) The weight to assign to the reliability versus reputation is arbitrarily set by the user, without considering the system evolution; (ii) the trust measure that an agent a perceives about an agent b is completely independent of the trust measure perceived by each other agent c, while in the reality the trust measures are mutually dependent. In this paper, we propose an extension of RRAF, aiming at facing the limitations above. In particular, we introduce a new trust reputation model, called TRR, that considers, from a mathematical viewpoint, the interdependence among all the trust measures computed in the systems. Moreover, this model dynamically computes a parameter measuring the importance of the reliability with respect to the reputation. Some experiments performed on the well-known ART(Agent Reputation and Trust) platform show the significant advantages in terms of effectiveness introduced by TRR with respect to RRAF. C 2011 Wiley Periodicals, Inc.

Forming groups of agents is an important task in many agent-based applications, for example when determining a coalition of buyers in an e-commerce community or organizing different Web services in a Web services' composition. A key issue in this context is that of generating groups of agents such that the communication among agents of the same group is not subjected to comprehension problems. To this purpose, several approaches have been proposed in the past in order to form groups of agents based on some similarity measures among agents. Such similarity measures are mainly based on lexical and/or structural similarities among agent ontologies. However, the necessity of taking into account a semantic component of the similarity value arises, for example by considering the context in which a term is used in an agent ontology. Therefore we propose a clustering technique based on the HISENE semantic negotiation protocol, using a similarity value that has lexical, structural and semantic components. Moreover, we introduce a suitable multiagent architecture that allows computing agent similarities by means of an efficient distributed approach.

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Web recommender systems are Web applications capable of generating useful suggestions for visitors of Internet sites. However, in the case of large user communities and in presence of a high number of Web sites, these tasks are computationally onerous, even more if the client software runs on devices with limited resources. Moreover, the quality of the recommendations strictly depends on how the recommendation algorithm takes into account the currently used device. Some approaches proposed in the literature provide multidimensional recommendations considering, besides items and users, also the exploited device. However, these systems do not efficiently perform, since they assign to either the client or the server the arduous cost of computing recommendations. In this article, we argue that a fully distributed organization is a suitable solution to improve the efficiency of multidimensional recommender systems. In order to address these issues, we propose a novel distributed architecture, called MUADDIB, where each user's device is provided with a device assistant that autonomously retrieves information about the user's behavior. Moreover, a single profiler, associated with the user, periodically collects information coming from the different user's device assistants to construct a global user's profile. In order to generate recommendations, a recommender precomputes data provided by the profilers. This way, the site manager has only the task of suitably presenting the content of the site, while the computation of the recommendations is assigned to the other distributed components. Some experiments conducted on real data and using some well-known metrics show that the system works more effectively and efficiently than other device-based distributed recommenders.