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[swc], [off , rhc] labeled with dc. in Visual Studio .NET Generate PDF417 in Visual Studio .NET [swc], [off , rhc] labeled with dc.

[swc], [off , rhc] labeled with dc. Using Barcode encoder for .NET Control to generate, create PDF 417 image in Visual Studio .NET applications. barcode 39 Consider the node [ .NET PDF417 off , rhc]. There are two actions that can achieve off , namely mc from cs and mcc from lab.

There is one action that can achieve rhc, namely puc. However, puc has as a precondition cs rhc, but cs and off are inconsistent (because they involve different assignments to the variable RLoc). Thus, puc is not a possible last action; it is not possible that, immediately after puc, the condition [off , rhc] holds.

Figure 8.3 shows the rst two levels of the search space (without multipath pruning or loop detection). Note that the search space is the same no matter what the initial state is.

The starting state has two roles, rst as a stopping criterion and second as a source of heuristics.. 8. Planning with Certainty The following examp .NET PDF417 le shows how a regression planner can recognize what the last action of a plan must be. Example 8.

10 Suppose the goal was for Sam to not want coffee and for the robot to have coffee: [swc, rhc]. The last action cannot be dc to achieve swc, because this achieves rhc. The only last action must be puc to achieve rhc.

Thus, the resulting goal is [swc, cs]. Again, the last action before this goal cannot be to achieve swc because this has as a precondition off , which is inconsistent with cs. Therefore, the second-to-last action must be a move action to achieve cs.

A problem with the regression planner is that a goal may not be achievable. Deciding whether a set of goals is achievable is often dif cult to infer from the de nitions of the actions. For example, you may be required to know that an object cannot be at two different places at the same time; sometimes this is not explicitly represented and is only implicit in the effects of an action, and the fact that the object is only in one position initially.

To perform consistency pruning, the regression planner can use domain knowledge to prune the search space. Loop detection and multiple-path pruning may be incorporated into a regression planner. The regression planner does not have to visit exactly the same node to prune the search.

If the goal represented by a node n implies a goal on the path to n, node n can be pruned. Similarly, for multiple-path pruning, see Exercise 8.11 (page 369).

A regression planner commits to a particular total ordering of actions, even if no particular reason exists for one ordering over another. This commitment to a total ordering tends to increase the complexity of the search space if the actions do not interact much. For example, it tests each permutation of a sequence of actions when it may be possible to show that no ordering succeeds.

. Planning as a CSP In forward planning PDF 417 for .NET framework , the search is constrained by the initial state and only uses the goal as a stopping criterion and as a source for heuristics. In regression planning, the search is constrained by the goal and only uses the start state as a stopping criterion and as a source for heuristics.

It is possible to go forward and backward in the same planner by using the initial state to prune what is not reachable and the goal to prune what is not useful. This can be done by converting a planning problem to a constraint satisfaction problem (CSP) and using one of the CSP methods from 4. For the CSP representation, it is also useful to describe the actions in terms of features to have a factored representation of actions as well as a factored representation of states.

The features representing actions are called action features and the features representing states are called state features..
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