Download Modeling Decision for Artificial Intelligence: 8th by Jordi Herrera-Joancomartí, Cristina Pérez-Solà (auth.), PDF

By Jordi Herrera-Joancomartí, Cristina Pérez-Solà (auth.), Vicenç Torra, Yasuo Narakawa, Jianping Yin, Jun Long (eds.)

This ebook constitutes the court cases of the eighth foreign convention on Modeling judgements for man made Intelligence, MDAI 2011, held in Changsha, China, in July 2011.
The 25 papers awarded have been rigorously reviewed and chosen from forty three submissions. the amount additionally includes prolonged abstracts of the 3 invited papers. the subjects lined are aggregation operators and selection making; clustering and similarity; computational intelligence; and information privacy.

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It mainly consists of four parts: data collection, feature conversion, hierarchical evidential fusion and output. The source data are collected from multiple heterogeneous sensors, and converted into two traffic state features, mean speed and volume. Then the features are fused in hierarchy by DS Evidence Theory for adapting to parallelization. The fusion part can be further divided into two levels, sub-fusion and main-fusion, which make use of the same algorithm on different input. Finally, the fusion results are used to evaluate the traffic states which are regarded as the output of HEFM.

The scheduling can be implemented by Globus Resource Allocation Management (GRAM) [23] package deployed on our cluster, and achieve workload-balance among all nodes. 1 Experiments Setup The experiments are carried out on the cluster of Zhejiang University Campus Grid (ZJU-CG), which combines high performance computing resources in the university, and provides a platform for solving scientific computation problems. 0 OS. As a test case, the region of Shanghai downtown with 393 roads is decomposed into three different granularities, 16, 64 and 256 sub-regions, which are implemented as different depths of the quadtree.

Let M1 := {a, a, a, b, b, c}, ΦX (M1 ) = 360; M2 := {a, a, b, c}, ΦX (M2 ) = 60; M3 := {a, b, c}, ΦX (M3 ) = 30; ΦX (M4 ) = 125, M4 := {c, c, c}, M5 := {b, b, b, b, c, c, c}, ΦX (M5 ) = 10125. Then M1 and M2 are comonotonic. We have that M3 ⊆ M4 and M3 ⊇ M4 , but M3 and M4 are comonotonic anyway. If comonotonicity had been an equivalence relation, then the fact that M2 and M3 are comonotonic and the transitivity of the equivalence relation would have implied the comonotonicity of M2 and M4 . However M2 and M4 are not comonotonic, hence illustrating the abscence of transitivity.

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