Title :
Multiresolutional hierarchical decision support systems
Author_Institution :
Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA
Abstract :
Decision support systems gain better performance and higher accuracy by the virtue of building multiresolutional (multigranular, multiscale) representation, and employing multiscale behavior generation subsystem (planning and control). The latter are equipped by devices for unsupervised learning that adjust their functioning to the results of self-identification. We demonstrate that planning and learning are joint processes. The author´s intention is to emphasize that the concepts of multiresolutional representation (MR) and multiresolutional decision support (MR-DSS) probably have in common a general significance that crosses the boundaries of particular domains of applications and disciplines. The paper explores this phenomenon. The ubiquity of a principle that somehow persistently delivers benefits to many areas of knowledge and technology seems to be more important than a habit to follow the pigeonhole principle of paper presentation.
Keywords :
decision making; decision support systems; generalisation (artificial intelligence); knowledge management; decision support systems; dynamic programming; generalization; indistinguishability; instantiation; knowledge; knowledge management; multigranular; multiresolutional; multiresolutional decision support; multiresolutional representation; multiscale; Control systems; Decision support systems; Dynamic programming; Image edge detection; Image resolution; Paper technology; Performance gain; Process planning; Signal resolution; Unsupervised learning;
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
DOI :
10.1109/TSMCC.2003.809866