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technique selected must allow for gradual application, as the information is known progressively and in real time Evolutionary techniques permit this gradual learning process by controlling the number of generations of evolution for a given content of the STM Thus, if evolutions last just a few generations per iteration (interactions with the environment), gradual learning by all the individuals is achieved To obtain general modeling properties in the MDB, the population of the evolutionary algorithm must be maintained between iterations (represented in Figure 161 through the world, internal, and satisfaction model blocks that are connected with the learning blocks), leading to a sort of inertia learning effect where what is being learned is not the contents of the STM in a given instant of time, but of sets of STMs that were previously seen.

In addition, the dynamics of the real environments where the MDB will be applied imply that the architecture must be intrinsically adaptive This strategy of evolving for a few generations and maintaining populations between iterations permits a quick adaptation of models to the dynamics of the environment, as we have a collection of possible solutions in the populations that can be easily adapted to the new situation In the rst versions of the MDB, the evolution of the models was carried out using standard canonic genetic algorithms where the ANNs were represented by simple twolayer perceptron models These tools provided successful results when dealing with simple environments and tasks, but as soon as real-world learning problems were faced, they were insuf cient Standard genetic/evolutionary algorithms, when applied to these tasks, tend to converge toward homogeneous populations, that is, populations where all of the individuals are basically the same.

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Click the General tab. Here you can choose whether or not to allow your kids to view Web sites that do not have a content rating (see Figure 15-5).

In static problems this would not be a drawback if this convergence took place after reaching the optimum Unfortunately, there is no way to guarantee this, and diversity may be severely reduced long before the global optimum is achieved In dynamic environments, where even the objective may change, this is quite a severe drawback In addition, the episodic nature of real-world problems implies that whatever perceptual streams the robot receives could contain information corresponding to different learning processes or models that are intermingled (periodically or not), that is, learning samples need not arise in an orderly and appropriate manner Some of these sequences of samples are related to different sensorial or perceptual modalities and might not overlap in their information content; others correspond to the same modalities but should be assigned to different models.

The problem that arises is how to learn all of these different models, the samples of which are perceived as partial sequences that appear randomly intermingled with those of the others Most traditional learning algorithms fail in this task as they have mostly been designed for learning a single model from sets of data that correspond to that model and, at most, some noisy samples or outliers within the training set This problem becomes even more interesting if we consider that it would be nice to be able to reuse some of the models, or at least parts of them, that have been successful in previous tasks in order to produce models for more complex tasks in an easier and more straightforward manner The existing approaches for applying evolutionary algorithms to dynamic problems, mainly optimization, can be grouped into two types [25]: memory-based approaches and search-based approaches.

In the rst group, the algorithm includes some kind of memory structure that stores information that can be used in the future to improve the optimization This memory may be internal, that is, included in the chromosomes [26, 27] and.

The introduction of VoIP in enterprises not only leads to convergence of multiple disparate networks in one physical infrastructure running only one (ie, IP) protocol, it also opens up the network for delivering several new and emerging productivity-enhancing IP-based applications and services to the employees and customers of the enterprises These new services include IP-based fax and conferencing services, uni ed messaging, nd-me/follow-me services, Web-based call/contact centers, e-commerce and customer-care services, support of virtual or remote or tele-workers, and so on Although the operational and infrastructure cost savings are the prime motivations for incorporating VoIP services in enterprises, there are other factors that contribute equally to the decision Some of these are (a) use of a uniform (ie.

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It is an empirical question whether curricula like those of liberal arts colleges best meet the ideals of liberal education. Is it conceivable that for some students, occupationally oriented programs may provide a better road to personal soundness than the traditional curricula of liberal arts colleges (p. 202)

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