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TABLE 6-2 Scene
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Probability of Proximity (P p) Of ce 0.99 0.99999 0.9 0.9 0.1 0.1 Home 0.1 0.3 0.99 0.5 0.01 0.1 Car 0.0 0.0 0.99 0.7 0.05 0.9 Restaurant 0.01 0.01 0.99 0.5 0.01 0.1
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ISP PSTN Cellular (urban) Cellular (rural) Cellular (remote) Iridium (remote)
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than zero, which is the probability of wireline access from the car, the boat, and the beach. In other words, while the quantitative measures of QoS of Table 6-1 make wireline bit pipes appear much better than wireless bit pipes, these metrics don t account for the user s experience. Proximity probabilities in Table 6-2 are illustrative, based on the assumption that the ISP and PSTN are BlueTooth enabled so the CWPDA can access wireline and wireless modes for the user. In this model the CWPDA must be within 2 meters of an access point and personal of ces are less than 4 meters across; homes have at least two rooms wired with PSTN out of a six-room house, but only one has an ISP port (e.g., DSL). Cellular and satellite telephone probability of proximity is estimated as probability of network connectivity. If the values of the QoS in Table 6-1 were weighted for the probability of proximity, the wireline values become nearly zero much of the time in a business day. This metric accurately re ects the dramatic success of cellular services in the 1990s with the right combination of price and performance for the marketplace and cultural inclinations. A CWPDA takes into account the situation dependence of the probability of connection. This strategy is expressed computationally in the QoI metric augmented with Tables 6-1 and 6-2 so CWPDA s can use <RF/> accordingly, seeking out WLAN-ISP access points, for example. Knowledge of the likelihood of RF connectivity of various types underlies nearly all the RF use cases. Although all the knowledge could be learned, AACR evolution may be accelerated via a priori models updated by experience. The a priori knowledge includes abstract models of the scenes with QoS and QoI estimates in stereotypical scenes. These models may be updated by the CWPDA s ability to connect experience in a given scene, such as in an of ce or home. Expression 6-2 <Scenes/> Represent Stereotypical QoS <Abstractions> <Scene/> <Scene> <Generic-setting/> <Of ce/> <Home/> <Car/> <Restaurant/> <Pedestrian/> <Traf c-accident/> <Earthquake/> <Severe-weather/> </Scene>
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<Scene> <Generic-setting> <Mode/> </Generic-setting> </Scene> <Abstractions/> The CRA <Self/> scene abstractions de ne <Scene/> as one or more places like the of ce, home, or car to be parameterized by QoS and probability & proximity through experience. A <Pedestrian/> scene may characterize the mobility of the AACR but does not correspond to a speci c place. Other generic scenes may be characterized by physical and social imperatives such as <Traf c-accident/>, <Earthquake/>, and <Severe-weather/>. In each of these scenes, human actors with AACRs are characterized by roles, such as <Injured/> or <Care-giver/>. The scene <Generic-setting/> is a place-holder to which <Scene/> attributes may be assigned through a discovery process. Each <Scene/> has one or more communications <Modes> and a probability of mode presence in a scene. The CRA doesn t require probability per se representing instead relative frequency of occurrence K/N, integer data that is easier to learn and on which probability-like inferences can be based. These may be integrated in reinforced hierarchical sequences. 6.1.3 Reinforced Hierarchical Sequence Describes RF Context
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In the CRA whenever a stimulus is encountered, both the number of times that stimulus has been observed and the number of cognition cycles since last observation are updated in the CRA inference hierarchy to form reinforced hierarchical sequences (RHSs). These observations are referenced in RHSs to time and space. Expression 6-3 Scene Model Showing RHSs
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<Scene> <Of ce> <Observations> 1000 </Observations> <ISP-WLAN> <OBS> 990 </OBS> </ISP-WLAN> <PSTN-BT> <OBS> 999 </OBS> </PSTN-BT> . . . </Of ce> </Scene> The probabilities of Table 6-2 are readily expressed as RHS ratios of the presence of a communications mode in a scene (<OBS/>), reinforced independently of the experience of the scene itself (<Observations/>). Speci cally, in Expression 6-3, the probability that a BlueTooth PSTN connection is available in the of ce is approximated by the relative frequency of occurrence of the observation of PSTN-BT in <Of ce/> versus the experience of the <Of ce/> scene. As the number of encounters with <Of ce/> increases, the precision of the reinforcements interpreted as probabilities increases. With, say, two visits to an of ce and two observations of <PSTN-BT>, one could write P(Of ce PSTN-Terminal) 1.0, interpreting relative frequency as an estimate of the probability of the joint events Of ce PSTN-BT. Keeping the raw counts of observations enables alternate hypothesis analysis
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