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In reference 18, it is assumed that the characteristic pro le can be accurately predicted over the time interval of T They model the dynamic of the networks as space time graph Routing algorithms in the constructed space time graph are developed using dynamic programming and shortest-path algorithm The routing algorithm nds the best route for messages by looking ahead The idea of the time layers in the space time graph comes from time-expanded graphs [43] The time-expanded graphs approach translates a problem of network ow over time to a classical static network ow, and standard tools of graph theory such as the Floyd Warshall algorithm can be applied to compute the shortest path for a source destination pair Figure 84 gives a link active schedule and for link AC, a space-time graph is illustrated in Figure 85.

Electronics packages have been trained to do this by neural networks [282] With LCS extracted from a <Scene/> one may evolve levels of cognition suggested in Table 11-2 In the rst entry of Table 11-2, the PDA senses location but doesn t use that data itself In the second entry, the behavior is preprogrammed These are good examples of AACR location-adaptive behavior, but there is no <Self/> and no machine learning so there is no cognition In the third case, the AACR applies general rules for the conservation of power (RDH) to its own battery state (RKO) to determine that it should turn off the WLAN since it is not likely to be connected to the corporate WLAN soon on its current <Path/> Interacting with the <User/> reinforces major decisions retained in a UKO, guided by UDH, a high level form of AML by discovery with reinforcement.

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In all these approaches under the deterministic case, an end-to-end path (possibly time-dependent) is determined before messages are actually transmitted However, in certain cases, the topology of the network may not be known ahead of time In the following section, we review some of the protocols designed for stochastic or random networks..

t=5

Toward such ends, the CRA structures AACR into AML-enabling functional components mediated in part through LCS sketches (Table 11-3) AACRs encounter con icting contextual cues from vision and other sensors that interfere with their ability to behave appropriately If the vision.

HARVEY: Great question! Harry, in those three hundred trades, did you tally the wins and the losses ME: Sure did Two hundred thirty-two winners and seventy-eight losers That means that over the course of a few hundred trades, I am likely to have 77 percent winning trades GINI: So this is a good system A really good one ME: Right But it wasn t the best I did better on the four-hour chart than any other I tested over six hundred trades across the same currency pairs I ended up with an average winner of 267 pips and an average loser of 94 I had a 74 percent win percentage as well HARVEY: Impressive Is that the chart you are going to focus on now with live trading ME: Yes, as soon as I m ready Until then I am going to keep paper trading HARVEY: Good.

t=4

Contributions of Functional Components to LCS Contribution to LCS Identity of people in the scene, beliefs, desires, and intent of user Asserts location, temperature, acceleration, compass direction, and so on Goals of media-independent services like playing a network game RF access opportunities and SDR state parameters Symbol grounding; system state, plans, reinforcement Speech synthesis; text, graphics, and multimedia displays

t=3

User sensory perception Local environment System applications SDR subsystem Cognition functions Local effectors

3 2

We ll talk about that in a moment For now, I have more questions for the two of you ME: Okay GINI: All right HARVEY: Gini, what if Harvey starts trading, let s say, this week, and for the rest of April he only has losses He has ten trades and all are losers What should he do GINI: I m not sure what you want me to say I think I would tell him to keep trading HARVEY: Why GINI: Because over time he is going to make money with the system that he tested HARVEY: That s right That s a brilliant answer But, Harry, what s the other side of that answer What are we assuming about your trades ME: We re assuming that I am only risking very small amounts on each trade, so ten losses really does not damage the account.

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