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Levels of a multiresolutional ELF are nested one within another, while the levels continue to function as separate independent ELFs This separation of levels is a result of a need to reduce the complexity of computations Thus, instead of solving in one shot the whole problem with the maximum volume of the state space and with the amount of high-resolution details, one may choose to solve several substantially simpler problems that are nested one within another 111011 Learning The process of generalization upon the time-varying functions of a control system is called learning It results in creating and constantly updating the multiresolutional system of representation and, thus, in an improvement of plans and feedback control laws Learning is a component of this multiresolutional knowledge processing Evolution of knowledge of the system can be demonstrated as shown in Figure 1118.

Obviously, learning is tightly linked with the property of Intelligent Systems of being equipped by the systems of knowledge representation (eg, the module World Model in ELF) Another question is that the module of representation might not necessarily be physically lumped in one speci c place: WM can be distributed over a multiplicity of agents, or otherwise over the physical medium used in the intelligent system Updating the World Model and the enhancement of its multiresolutional system of knowledge representation is done by the process of learning, which employs the set of GFACS operators that has been described above Levels of resolution are selected to minimize the complexity of computations Planning and determining the bene cial feedback control laws is also done by a joint using of generalization, focusing attention, and combinatorial search (GFACS) The operation of learning was associated with layers: Each layer learns separately.

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Learning experiences can be organized only by using a multiresolutional structure Levels are not hard-wired; they are constructed from the information at hand, as is done in neural nets, for example..

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QoE and QoS monitoring in cellular networks consists of collecting/processing service and network performance statistics, usage data and QoS-related faults. In order to obtain end-to-end quality of service monitoring, network elements (NEs), the element management layer (EML) and network management layer (NML) must all be involved

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placing the Universe within a few percent of spatial flatness and completely ruling out significantly non-flat Universes. While this conclusion does depend on the validity of the assumptions made in the theoretical calculation of the CR., those predictions are borne out so well by the observations that discussion of non-flat Universes has become very rare. Figure A2A on page 132 shows an earlier constraint in the nO-n A plane from the Boomerang and Maxima experiments (constraints from WMAP would be similar but tighter). Note how the preferred region accurately follows the line indicating flat geometry. Only a tiny region, the location of the Standard Cosmological Model, can match both the microwave background and supernova data. Combining the microwave data with the preferred matter density of no c:::: 0.3 also gives support for the cosmological constant independent of the supernova results.

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