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The choice of K captures the tradeoff between responsiveness and steady-state error. In reference 6 the range of possible settings for Uth was characterized. Each SMAP has Ns -associated MSs, each fed by a Poisson packet arrival process. The default simulation parameters used are shown in Table 12.4. In Figures 12.8 and 12.9 we show the tradeoff between packet mean delay and power consumption for an SMAP that runs the adaptive protocol in comparison to another SMAP that is using a xed boundary for the contention period. The xed boundary scheme was originally proposed in reference 7. In the xed boundary scheme we show the curves corresponding to 50% and 75% of the SMAP time being spent in the CP interval. The capacity of the SMAP in these cases is mandated by the xed boundary. It can be seen that the power consumption stays relatively at, with minor variations due to the different times spent in transmit and receive modes. The power consumption of the SMAP associated with a 75% xed boundary is 50% higher than that of the 50% xed boundary case. Therefore, it can be seen that the power consumption in the xed boundary case scales proportionally to the activity interval.

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If <Genie/> advises the CWN of the change of plan, then it will be able to advise of a more ef cient spectrum-use plan for staying connected by <TV-WLAN/> as the vehicles transit different neighborhoods on entirely different time lines to visit Ed and Diane, stay the night, and be on their way in the morning However, the technology challenges of autonomously maintaining primal sketches becomes clearer if <Genie/> is new to the family and has never heard of Ed or Diane Introducing new types of knowledge to AACR is much more dif cult than merely instantiating known types of knowledge into a primal sketch The successful solution of this class of problem without the mediation of expensive knowledge engineers calls for the autonomous incremental knowledge acquisition for broad classes of task-related knowledge 112 AUTONOMOUS EXTENSIBILITY.

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TABLE 12.4. Default Simulation Parameters Parameter Superframe interval, TSF WLAN transmission rate Number of data stations per AP, Ns Data packet payload Power consumption in LISTEN/RECEIVE mode Power consumption in TRANSMIT mode Power consumption in DOZE mode Minimum duration of CP-subframe, tmin Value 100 ms 11 Mbps 20 200 bytes 500 mW 750 mW 2 mW 10 ms

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Did this one go up even further Did you regret closing it CRAIG: You bet I regretted it I had let my emotions ruin the trade I had picked 13100 as my profit target with a round number, and a point of support or resistance that the pair had stopped at in 1997, 1998, and 1999 If it got that high, it would be the first time in three years that it reached that level It would stop there for sure ME: Did it get there CRAIG: Damn right it did If I had held on, I could have made a lot more on that trade I would have had to hold it for four more months, but I would have made an additional thirty million dollars on this trade alone Forget a round of applause I would have been promoted to managing director or something wild like that.

Autonomous incremental knowledge acquisition is another technology that has existed since the 1980s but that for a variety of technical and economic reasons has yet to be fully deployed, at least not in the <User/> domain of AACR-class radio engineering. This section explores the foundations of

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autonomous extensibility for use in the exible aggregation of <User/>domain competence through interaction with <User/>s. Tieresias [318] was the rst autonomously extensible rule-based system. It was implemented as the autonomously extensible form of Mycin, the expert system that diagnosed bacterial infections of the blood. Some features of Tieresias appeared in applied expert systems in the AI boom of the 1980s, including tools like ART and KEE [284]. Principles were re ned and applied in agent technology, for example, in telecommunications in the 1990s [94, 285]. These applied expert systems generally did not exploit Tieresias methods that interactively interpreted schema schema to transfer knowledge. Tieresias itself was known to be brittle, extensible by those who deeply understood its internal data structures and KA quirks, but not readily extensible by the typical user. This section takes a closer look at the schema schema method of Tieresias for <User/>-domain knowledge customization for AACR. This provides a foundation for the embedding of autonomously extensible rule bases into the <Orient/>, <Plan/>, and <Decide/> phases of the CRA, realized in UKO, UDH, and LCS primal sketches to improve the robustness of autonomous extension for casual users. 11.2.1 Extensible Knowledge Bases

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