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The Distributed Vehicle Monitoring Testbed (DVMT) was one of the earliest and best-known testbeds for multiagent systems. The DVMT was a fully instrumented testbed for developing distributed problem-solving networks (Lesser and Erman, 1980; Lesser and Corkill, 1988). The testbed was based around the domain of distributed vehcle sensing and monitoring: the aim was to successfully track a number of vehicles that pass within the range of a set of distributed sensors. The main purpose of the testbed was to support experimentation into different problem-solving strategies. The distributed sensing domain is inherently data driven: new data about vehcle movements appears and must be processed by the system. The main problem with the domain was to process information as rapidly as possible, so that the system could come to conclusions about the paths of vehcles in time for them to be useful. To coordinate the activities of agents in the DVMT, Durfee developed an approach known as partial global planning (Durfee and Lesser, 1987; Durfee, 1988, 1996). The main principle of partial global planning is that cooperating agents exchange information in order to reach common conclusions about the problemsolving process. Planning is partial because the system does not (indeed cannot)

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The primary advantages of the expert knowledge in the database method for decision tree searching are maintainability, exibility and ease of debugging. A decision-tree searching programming is likely to require maintenance by adding, deleting, or modifying the node structure. The one-rule-per-hypothesis method of auto1.fps requires program modi cation by changing or adding rules, with all the error possibilities that this entails. The expert-database method of auto2 leaves the program intact, and requires only modi cation of the database. Further, the availability of FLEDIT as a database editing tool means that we have available a database editor automatically customized for our particular application. Flexibility comes about with the ability of the program to learn, if we wish, although the programming involved is not simple. We can modify the search direction by assigning hypothesis truth-values under program control. We even have the ability to add, delete, or modify hypotheses from the database under program control. Ease of debugging is achieved by the availability of FLEDIT to debug our database, since FLEDIT is automatically customized for each data item in each data element involved.

Throughout this book, we ve used the concept of broadcast quality to mean really, really good. However, anyone who has listened to AM radio knows that it doesn t sound anywhere near as good as FM, and for that matter FM radio doesn t sound as good as CDs. Yet they re both broadcast standards, and we still listen to radio, even AM. Different types of programming do not need as much fidelity as others. The idea, then, is to figure out how much fidelity your programming requires and produce content to that standard. When recording the content, you should always record at a very high standard, because that gives you the most flexibility later on. But when it comes time to encode your content for Internet distribution, you may want to sacrifice a bit of quality for the cost savings it provides. Table 11.1 lists some common bit rates offered by encoding software and brief descriptions of what quality you can expect using different encoding technologies.

2. Iluili2 # 0. In this case, it is easy to find at least one solution to u,Sw = plluil12e(i). Indeed, Sw" = pu,te(i) (10.16)

However, since all results in the sequel, until Sec. 41.1, will hold irrespective of any structure in u3.%, shall proceed with our arguments by treating the general case (40.14). we The (regularized) projection of yz onto R ( H 3 , , ) is given by (cf. (40.6)):

The background in the image is a little dull, so it would be good to lighten this a touch to lift the whole image. Do this by creating a mask for the background, then applying a Levels adjustment layer to just that area. Follow these steps to achieve this:

If the Levels command is a chisel for refining an image s tonal range, then the Curves command is a scalpel. With Curves, you can target small ranges of an image s tonal qualities with much more control than the Levels command. Figure 1.8 shows the Curves dialog box. Curves works along the same lines as Levels. You set the black point and the white point and then adjust midtone values. The main difference with Curves is that you have much greater control when adjusting midtone values. The interface can be intimidating, but after you understand what the various adjustments do, it begins to make sense. Let s give it a try with these steps:

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