Catálogo de publicaciones - libros

With the proliferation of content creation devices, sharing digital contents has become an increasingly common task in our daily lives. This research proposes the “Photo Agent” that helps users manage and share digital photos without explicit file manipulation and data communication. The agents shares photos autonomously and pro-actively, so users can simply specify which pictures to share with whom , rather than how the pictures are actually distributed and searched. The prototype photo agent utilizes peer-to-peer networking to support efficient content sharing in a distributed environment.

Reputation systems provide an incentive for cooperation in artificial societies by keeping track of the behavior of autonomous entities. The self-organization of P2P systems demands for the distribution of the reputation system to the autonomous entities themselves. They may cooperate by issuing recommendations of other entities’ trustworthiness. The recipient of a recommendation has to assess its truthfulness before taking it into account. The current assessment methods are based on plausibility considerations that have several inherent limitations. Therefore, in this paper, we propose social structure as a means of overcoming some of these limitations. For this purpose, we examine the properties of social structure and discuss how distributed reputation systems can make use of them. This leads us to the formulation of three hypotheses of how social structure overcomes the limitations of plausibility considerations. In addition, it is pointed out how the hypotheses can be tested.

A multiagent distributed system consists of a network of heterogeneous peers of different trust evaluation standards. A major concern is how to form a requester’s own trust opinion of an unknown party from multiple recommendations, and how to detect deceptions since recommenders may exaggerate their ratings. This paper presents a novel application of neural networks in deriving personalized trust opinion from heterogeneous recommendations. The experimental results showed that a three-layered neural network converges at an average of 12528 iterations and 93.75% of the estimation errors are less than 5%. More important, the model is adaptive to trust behavior changes and has robust performance when there is high estimation accuracy requirement or when there are deceptive recommendations.