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The many problems with symbolic/logical approaches to building agents led some researchers to question, and ultimately reject, the assumptions upon which such approaches are based. These researchers have argued that minor changes to the symbolic approach, such as weakening the logical representation language, will not be sufficient to build agents that can operate in time-constrained environments: nothng less than a whole new approach is required. In the mid to late 1980s, these researchers began to investigate alternatives to the symbolic A 1 paradigm. It is difficult to neatly characterize these different approaches, since their advocates are united mainly by a rejection of symbolic AI, rather than by a common manifesto. However, certain themes do recur: the rejection of symbolic representations, and of decision makmg based on syntactic manipulation of such representations; the idea that intelligent, rational behaviour is seen as innately linked to the environment an agent occupies - intelligent behaviour is not disembodied, but is a product of the interaction the agent maintains with its environment; the idea that intelligent behaviour emerges from the interaction of various simpler behaviours. Alternative approaches to agency are sometime referred to as behavioural (since a common theme is that of developing and combining individual behaviours), situated (since a common theme is that of agents actually situated in some environment, rather than being disembodied from it), and finally - the term used in this

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Figure 6-4

Framework for open planning and scheduling systems, based on O-Plan and TOSCA. Mainly oriented toward constraint processing. A model with eight layers. Scheduling problems classify in one of these layers based on products, resources, processes, timeframe, and constraints. A framework of automated planning and scheduling (ASPEN) in the spacecraft operations and control domain. Includes an object-oriented system with reusable software components, which can also be applied outside the spacecraft domain. An object oriented approach for scheduling space shuttle missions. Can also be applied in other domains. An object oriented framework with objects that occur in manufacturing scheduling problems.

The following is an XSQL page that invokes this serializer. It follows the same rules as those of a text serializer. The only thing required is that you must specify the serializer in the xml-stylesheet processing instruction. Since the XML input is ignored in the simple example, it doesn t matter what XSQL is used or whether a stylesheet precedes transforms the datagram.

All components are based on knowledge-based techniques; that is, problemspeci c knowledge is identi ed, represented, and applied for the solution of the addressed problem.

assumed and decision fedback error propagation is ignored. These low short-term BER estimates were obtained from the average values of Equation 10.14, which was plotted in Figure 10.14. of the previous section, the magnitude of the bit LLR can also be used as the modem mode switching metric. The turbo decoder iteratively improves the BER the decoded bits. Since of the average burst LLR magnitude before and after decoding has an approximately linear relationship, as demonstrated by Figure 10.12 in Section 10.4.2, the average probability of error for the decoded burst can be inferred from the average burst LLR magnitude provided by the RBF equalizer using Equation 10.16. Thus, this parameter can also be used as the switching metric of the turbo-coded BbB AQAM scheme. Figures 10.23 and 10.24 portray the average burst LLR magnitude fluctuation before and after turbo decoding, respectively, over the burst-invariant channel of Table 10.4 versus the symbol index for various QAM modes, as given by the RBF DFE and the turbo decoder, which is slowly varying and predictable for a number of consecutive data bursts. Therefore in our simulated channel scenario the average burst LLR magnitude both before and after turbo decoding constitute suitable metrics for the AQAM switching mechanism. The average burstLLR magnitude obtained from either the RBF DFE or the turbo decoder is compared to a set of switching LLR magnitudes corresponding to the modulation mode of a next that data burst. Consequently, modulation mode is selected for the transmission burst, based on the current estimated BER upon assuming slowly fading channels. More explicitly, this implies that the similarity the average burst LLR magnitude consecutive data bursts of of can be exploited, in order to set the next modulation mode. Again, the modulation modes

Figure 2.14 Cubic Lagrange-Gauss-Lobatto nodal shape functions; vertex functions 6iIi34 (left) and bubble functions 02, 03 (right).

1. 2. 3.

decreasing the current window to one packet, and entering the slow-start phase. More details about TCP congestion control are in 8. In Figure 6.12 the x-axis shows the total throughput as a fraction of the maximum possible throughput on the congested link. The y-axis shows the average queuing delay. The simulations with Tail drop router were run with the buffer size ranging from 15 to 140 packets. As the buffer size is increased, the throughput and the average delay increase correspondingly. To avoid phase effects w17, 18x in the simulations with tail drop routers, the source node takes a random time drawn from the uniform distribution on w0, t x to prepare an FTP packet for transmission, where t is the bottleneck service time of 0.17 ms w17x. The simulations with RED routers were all run with a buffer size of 100 packets, with min t h ranging from 3 to 50 packets. For the RED routers,