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This article proposes and compares different interaction models for reinforcement learning based on multi-agent system. The cooperation during the learning process is crucial to guarantee the convergence to a good policy. The exchange of rewards among the agents during the interaction is a complex task and if it is inadequate it may cause delays in learning(More)
This paper presents a novel method for the classification of images that combines information extracted from the images and contextual information. The main hypothesis is that contextual information related to an image can contribute in the image classification process. First, independent classifiers are designed to deal with images and text. From the(More)
Finding reliable partners to interact with in open environments is a challenging task for software agents, and trust and reputation mechanisms are used to handle this issue. From this viewpoint, we can observe the growing body of research on this subject, which indicates that these mechanisms can be considered key elements to design multiagent systems(More)