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in the practical shunting problem case of the Netherlands Railways (s 13, 14, 15, 16, and 17). Note that the AI approaches here differ from the ones discussed in the section about planning for yourself in AI and cognitive science, where actors make plans for themselves rather than for organizational processes. Quantitative models are usually based on an analysis of the objects (entities) the domain that are scheduled. For example, to make an algorithm for a planning problem in a ow shop, one has to know the capacities of machines, the setup and cleaning times, the number and sizes of orders, the processing characteristics, and so on. All these characteristics can be used to determine the best way to navigate through the problem space of possible solutions. An example of how such knowledge can be used in an algorithm is to start to plan the bottleneck rst. This is often the most sensible thing to do in order to avoid problems in a later stage of the planning process. Most techniques are somehow limited in the kinds of characteristics they can handle. For example, a linear programming model cannot deal with nonlinear constraints, and temporal reasoning is dif cult to implement in many mathematical techniques. Therefore, the domain analysis must somehow be translated into the quantitative model, and the solution must be translated back to the application domain. The second mainstream approach to plan generating techniques focuses on imitating human problem-solving processes in so-called rule-based, expert, or knowledge systems, also called the transfer view (Schreiber et al., 1993, 2000), because the knowledge to solve planning problems is extracted from a human and transferred into a computer program. For this approach, the problem-solving approach of the human scheduler must be analyzed. In terms of the human problem solver (Newell and Simon, 1972), this means that the problem space and operators must be traced and implemented. In the resulting plan generators, the available computational capacity is not used since the computer is used as a symbolic processor. It is, however, understandable for the human planner why a generated plan looks as it is, because he would have processed the symbols more or less in the same way. This approach can be used if a planner is satis ed with a reduction of the efforts without much improvement of the solution for example, if planning is a secondary task of the one who makes it. The main disadvantage of this approach is that the system inherits not only the capacity of abstract reasoning that is so typical of humans, but also the myopic re ghting tactics that human schedulers practice (Smith, 1992). In addition, the resulting algorithms are highly speci c for the individual human planner. We developed a graded architecture for planning support in which, on the one hand, support consists of doing many things by hand on the computer and, on the other hand, consists of algorithms that can be invoked to do complex computations for the planner (van Wezel and Jorna, 1999; van Wezel, 2001). The structure consists of an electronic planning board (editor), a constraints checker (inspector), a goal function evaluator (evaluator), and an algorithmic component (generator). We discussed this in s 4 (Jorna), 7 (van Wezel), 13 (Kiewiet et al.), and 17 (Riezebos et al.).
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You don t need to escape most of the previously mentioned special characters when they re inside a character class. The exceptions are the caret, which still needs to be escaped (unless you re using it to negate the class as just shown), and the backslash, which is used for specifying shorthand character classes, as you see in a moment. You can also use various shorthand character classes comprising a backslash followed by one of several letters, as follows:
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