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Titlebook: Advances in Artificial Intelligence; 28th Canadian Confer Denilson Barbosa,Evangelos Milios Conference proceedings 2015 Springer Internatio

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https://doi.org/10.1007/3-211-38173-2ent where the number of colored spots which represent tasks and the ratios of them are unknown. The robots should discover the spots cooperatively and spread proportional to the spots area. We proposed 4 self-organized distributed methods for coping with this scenario. In two different experiments the performance of the methods is analyzed.
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https://doi.org/10.1057/9781137462275esian network inference using Lazy Propagation. In particular, we describe how the semantics of potentials created during belief update can be determined using the Semantics in Inference algorithm. This includes a description of the necessary properties of Semantics in Inference to make the task fea
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Brazilian Bodies and Nationalism in Dancesurprisingly simple, yet remarkably robust. With respect to modeling, on one hand, DNs not only represent BNs, but also faithfully represent the testing of independencies in a more straightforward fashion. On the other hand, with respect to two exact inference algorithms in BNs, DNs simplify each of
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https://doi.org/10.1007/3-211-38173-2ent where the number of colored spots which represent tasks and the ratios of them are unknown. The robots should discover the spots cooperatively and spread proportional to the spots area. We proposed 4 self-organized distributed methods for coping with this scenario. In two different experiments t
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https://doi.org/10.1007/3-211-38173-2 competing one-class Support Vector Machine models, one for each data class. The presented approach enjoys three budget-driven features: 1) it is capable of handling classification when data cannot fit in memory; 2) both training and labeling process is user controllable; 3) the classifiers can easi
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