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recommendations for care and cosmetics suitable for the customer s skin. Skin-CRM suggests AACR humanitarian applications in indigent personal hygiene, diagnosing skin conditions, remotely analyzing wounds, and otherwise enabling people who cannot physically arrive on a scene to apply specialized expertise via wireless information services. Derivative commercial and military AACR use cases could combine patient tags, equipment tags, digital cameras, and wireless connectivity for point-of-action information services tailored to communications and network server capabilities.
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s of this text develop the technologies needed to learn from experience so that future CPDAs based on AACR technology do not fall victim to the expense of a huge logistics tail of hundreds or thousands of knowledge engineers needed to keep up with a customer base. This was called the knowledge engineering bottleneck in the 1980s. Hopefully the use cases above and the related exercises demonstrate the relative futility of anticipating all the permutations and combinations of knowledge needed to customize each AACR for the speci cs of the radio environment. On the other hand, even this introductory set of radio-domain oriented use cases suggests a trade-off between a priori knowledge and ML, autonomous or not. Well engineered products will tend toward fewer, simpler, and easier to understand functions. The kids (the computer scientists) may get it before the parents (the RF engineers). But even the parents will see through a product that really isn t very smart, even if it says Cognitive on the label. Thus, the next chapter introduces the store of a priori knowledge that is foundational to bootstrapping radio knowledge by AML, while the subsequent chapters relate contemporary AML technologies to this prior knowledge, for lower cost and more exibility of AACR applications as the technologies mature. The radio knowledge chapter that follows consists of dozens of knowledge vignettes that focus mostly on the radio, especially on the potential connectivity, data rates, and networking opportunities offered by successful employment of the physical layer of the protocol stack. Subsequently, both radio knowledge and user-driven knowledge are formalized for AACR applications, which conclude the text. This chapter therefore builds a progression that, taken as a whole, introduces CR technology towards an evolution of AACR applications.
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6.1. Complete Expression 6-1 using the data from Table 6-1. 6.2. Describe in RXML the experience of reinforcement of path connectivity for 1000 attempts to connect if each attempt yields exactly one reinforcement of attempt and if connected yields only reinforcement success given that 80 con-
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nections occur out of 100 attempts on Path1 and only 70 connections occur out of 100 on Path2. Further develop a particular use case of this chapter by building your own analytical model of the use case. Extract those entities that will interact in order to achieve the demand-shaping aspect of the use case and describe them in RXML, inventing ontological primitives in your own taxonomy. Develop the XG UML use case under the assumption that spectrum management policy undergoes a period of rapid change. At rst, the FCC advocates the use of unused TV channels for ISM networks. There are heart monitors already that use those bands. Suppose the FCC s policy enables any reasonable use, but industry decides to defer to legacy medical users in order to avoid civil liability. Model the impact of these changes on policy language and computational intelligence of deployed nodes (personal, vehicular, and infrastructure), with particular emphasis on the support costs. How much could it cost to maintain a staff of computer scientists, ontologists, and radio engineers to create the new personality downloads for the deployed FCC CR radios What is the impact on this logistics tail of machine learning (a) Include techniques from 4 on AML to create UML for this use case. What aspects of AML are not readily captured in UML What other software tools can capture such situations How can you work around the limitations of UML to represent learning (b) After reviewing the CD on CR1, teach CR1 a relevant subset of the XG policy language. How much can it learn by itself using its CBR What is the minimum set of additional CBR nodes needed to learn the heartmonitor example If you accomplished this by simply training CR1 about heart monitors without programming any new PDANode classes, then give yourself an A. If not, can your new PDANode class extend to other classes of radio What CASE tools are available for developing the use cases of this chapter What is their value How does that compare to the costs How do you measure the cost of the time it will take you to learn to use the tool Develop a UML model of your favorite use case from this chapter. (a) What are the entities of the UML model The user and the self of the CWPDA are clearly entities. What other entities are critical Should there be an entity for each member of a family If so, how will the CWPDA come to know those family members Same for a friend from work (b) What other entities should be in the UML model Is there a model for entities at work such as the boss or co-workers With conventional knowledge engineering technology, there is no right answer because almost no matter what you preprogram, you are wrong. Can your marketing department give you a good enough pro le to sell to early adopters (c) Write an algorithm by which your CWPDA could learn from experience who is present and which entities need to be represented with separate identities. This ability to learn from experience separates the cognitive from the merely aware adaptive radios. Answer the following question for the narrowband spectrum rental etiquette of the FCC spectrum rental use case. What is the relationship between the RF physical layer and the amount of computational intelligence in the nodes
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