rdlc data matrix

rdlc data matrix

rdlc data matrix

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Such problems have been considered in the AI literature on multi-agent systems in particular, concerning cooperation and negotiation (eg, Carbonell, 1981; Zlotkin and Rosenschein, 1985; Kreifelts and Martial, 1990; Georgeff, 1983; Durfee, 1988; Jennings, 1995) They have also been studied in game theory and formal veri cation under various forms (eg, Osborne and Rubinstein, 1994; De Alfaro, Henzinger, and Kupferman, 1998) The novelty of our work is twofold First, we introduce adversarial planning problems and a class of adversarial plans called strong cyclic adversarial plans An adversarial planning domain has a single system agent that is controllable and a single environment agent that is uncontrollable and may be an opponent to the system agent The task is to synthesize plans for the system agent that is robust to any plan of the environment agent.

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We go beyond theoretical work and present an OBDD-based planning algorithm that ef ciently can generate strong cyclic adversarial plans This algorithm has been fully implemented in the OBDD-Based Informed Planning and Controller Synthesis Tool (BIFROST) 07 (Jensen, 2003) Second, we formally de ne our concept of multi-agent planning equilibria for multi-agent domains inspired by the game theoretic notion of equilibria (Owen, 1995) In Section 102, we begin by presenting the foundations of our research on multiagent planning equilibria First, we demonstrate intuitively through simple examples that plan solutions depend on the goals of all the agents We also present the formal notion of a planning domain that we use throughout the chapter In Section 103, we introduce adversarial planning We present an algorithm for synthesizing adversarial plans and demonstrate theoretically and experimentally that the generated plans are robust to any strategy of the environment.

In Section 106, we generalize the notion of accounting for an adversary with the solution concept of multi-agent planning equilibria We formalize this notion for planning environments where all the agents goals are explicitly speci ed We also analyze this notion of equilibria in a variety of domains..

The business case in this example is to understand more clearly what content visitors accessed and interacted with. Many Web sites lack page URLs that provide useful, descriptive information about what content is on the page and where that page sits within the hierarchy of the site. For example, consider this URL: /modules.php name=Marketplace&file=item&ItemID=602492. Standard Google Analytics tracking would report exactly that URL not terribly insightful as to what content the visitor was viewing. To make this information more useful, you could instead use custom variables to create content group names. Table 7.2 shows an example content group model. The data model in Table 7.2 articulates a means of recording section, group, subgroup, page name, and tag data on a page-level scope when the page is viewed, and setting a visitor level-scope to identify that this visitor has viewed a product. Consider creating a similar data model before implementing any code for custom variables to plan requirements, opportunities, and analysis methods. Custom variables are a feature that is truly enterprise-level and thus implementation requirements should not be taken lightly poor planning of custom variables will result in poor data, while thorough evaluation of needs and precise implementation of this feature will yield powerful insights. You can learn more about the technical side of custom variables in 5 and visit www.analyticsformarketers.com/s/custom-variables for links to additional online resources.

Most 2D commands accept 3D coordinates (a coordinate that includes a Z value) only on the first point. After that, you can omit the Z coordinate because the Z value will be the same as that of the first point. For example, if you draw a rectangle, you can specify its first corner as 2,3,8 but the second corner can be specified without the Z value, as in 6,7. The Z value for the opposite corner is automatically 8. The LINE command is an exception. It is a true 3D command, so you can specify X, Y, and Z values at all points.

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