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Software engineers have derived a progressively better understanding of the characteristics of complexity in software. It is now widely recognized that interaction is probably the most important single characteristic of complex software. Software architectures that contain many dynamically interacting components, each with their own thread of control and engaging in complex, coordinated protocols, are typically orders of magnitude more complex to engineer correctly and efficiently than those that simply compute a function of some input through a single thread of control. Unfortunately, it turns out that many (if not most) realworld applications have precisely these characteristics. As a consequence, a major research topic in computer science over at least the past two decades has been the development of tools and techniques to model, understand, and implement systems in which interaction is the norm. Indeed, many researchers now believe that in the future, computation itself will be understood chiefly as a process of interaction. Just as we can understand many systems as being composed of essentially passive objects, whch have a state and upon which we can perform operations, so we can understand many others as being made up of interacting, semiautonomous agents. T h s recognition has led to the growth of interest in agents as a new paradigm for software engineering. As I noted at the start of this chapter, the trend in computing has been - and will continue to be - toward ever more ubiquitous, interconnected computer systems. The development of software paradigms that are capable of exploiting the potential of such systems is perhaps the greatest challenge in computing at the start of the 21st century. Agents seem a strong candidate for such a paradigm.



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a the resource request could be made more general in the sense that some requests might require multiple instances of certain resources; the claim sets would then become multisets and the multiplicities of the arcs from the resource-places to the grabtransitions would have to be changed accordingly. We finally comment on the solution of this type of SPN. Although a straightforward solution via the underlying CTMC is, in principle, always possible, in many practical cases the underlying CTMC becomes too large to be generated and evaluated, especially if R, K, and the values of n, and ml, are not small. Therefore, various researchers have looked for approximations for this type of model. In particular, if the transitions requestk and grabk are amalgamated (and place waitl, is removed, as in Figure 16.17) the SPN has a structure that allows for a product-form solution of a similar form to that seen for GNQNs





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For instance, female boxers reported deliberately wearing feminine uniforms in the ring, emphasizing feminine aspects of their appearance (eg, wearing makeup and having long hair), and concealing information about their sexuality to avoid undesired impression formation With regard to exercise, a handful of first- and secondgeneration studies have examined the types of impressions associated with being an exerciser (eg, Martin, Sinden, & Fleming, 2000; Martin Ginis, Latimer, & Jung, 2003) Borrowing a methodology used in stereotype studies, these investigations have involved presenting respondents with a written description of a male or female target Information about the target s exercise habits is embedded in the description and manipulated across conditions (eg, the target is described as an exerciser or a nonexerciser, or no.

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at away from and that of the JacobianRBF DFE TEQ three iterations is about 2dB and 2.5dB the zero-IS1 Gaussian BER curve for BPSK and 4-QAM, respectively, at a BER of lop4. For 16-QAM, the effect of error propagation degrades the performance of the Jacobian RBF DFE TEQ by 4dB at BER of lop4, when we compare the Jacobian RBF DFE TEQ's correct feedback based and decision feedback assisted performance after the final iteration, as seen in Figure 1 1.10. Again, the performance can be improved by increasing the equaliser's feedforward order at the expense ofa higher computational complexity, as discussed in Section 11.5.1. The iteration gain of the Jacobian RBF DFE TEQ after the final iteration at a BER of was 2dB, 3dB and more than 15dB for the modulation modes of BPSK, 4-QAM and 16-QAM, respectively. By contrast, for the Log-MAP TEQ the corresponding iteration gains were OSdB,1dB and 2dB for the modulation modes of BPSK, 4-QAM and 16-QAM, respectively. Explicitly, the iteration gain was defined as the difference between the channel SNR required in order to achieve a certain BER after one iteration and the correspondingchannel SNR required after n number of iterations. The iteration gain was higher for the higher-order

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