Hierarchical Control in Visual Studio .NET Create PDF417 in Visual Studio .NET Hierarchical Control

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Hierarchical Control generate, create qr barcode none with .net projects QR Codes One way that you cou QR Code for .NET ld imagine building an agent depicted in Figure 2.1 (page 45) is to split the body into the sensors and a complex perception system that feeds a description of the world into a reasoning engine implementing a controller that, in turn, outputs commands to actuators.

This turns out to be a bad architecture for intelligent systems. It is too slow, and it is dif cult to reconcile the slow reasoning about complex, high-level goals with the fast reaction that an agent needs, for example, to avoid obstacles. It also is not clear that there is a description of a world that is independent of what you do with it (see Exercise 1 (page 66)).

An alternative architecture is a hierarchy of controllers as depicted in Figure 2.4. Each layer sees the layers below it as a virtual body from which it gets percepts and to which it sends commands.

The lower-level layers are able to run much faster, react to those aspects of the world that need to be reacted to quickly, and deliver a simpler view of the world to the higher layers, hiding inessential information. In general, there can be multiple features passed from layer to layer and between states at different times. There are three types of inputs to each layer at each time:.

the features that come from the belief state, which are referred to as the remembered or previous values of these features; the features representing the percepts from the layer below in the hierarchy; and the features representing the commands from the layer above in the hierarchy.. 2.3. Hierarchical Control high-level percepts high-level commands next state low-level commands Agent Environment previous state low-level percepts Figure 2.4: An ideal ized hierarchical agent system architecture. The unlabeled rectangles represent layers, and the double lines represent information ow.

The dotted lines show how the output at one time is the input for the next time.. There are three type VS .NET QR Code s of outputs from each layer at each time:. the higher-level p ercepts for the layer above, the lower-level commands for the layer below, and the next values for the belief-state features.. An implementation of .NET QR Code ISO/IEC18004 a layer speci es how the outputs of a layer are a function of its inputs. Computing this function can involve arbitrary computation, but the goal is to keep each layer as simple as possible.

To implement a controller, each input to a layer must get its value from somewhere. Each percept or command input should be connected to an output of some other layer. Other inputs come from the remembered beliefs.

The outputs of a layer do not have to be connected to anything, or they could be connected to multiple inputs. High-level reasoning, as carried out in the higher layers, is often discrete and qualitative, whereas low-level reasoning, as carried out in the lower layers, is often continuous and quantitative (see box on page 52). A controller that reasons in terms of both discrete and continuous values is called a hybrid system.

. 2. Agent Architectures and Hierarchical Control Qualitative Versus Quantitative Representations Much of science and engineering considers quantitative reasoning with numerical quantities, using differential and integral calculus as the main tools. Qualitative reasoning is reasoning, often using logic, about qualitative distinctions rather than numerical values for given parameters. Qualitative reasoning is important for a number of reasons: An agent may not know what the exact values are.

For example, for the delivery robot to pour coffee, it may not be able to compute the optimal angle that the coffee pot needs to be tilted, but a simple control rule may suf ce to ll the cup to a suitable level. The reasoning may be applicable regardless of the quantitative values. For example, you may want a strategy for a robot that works regardless of what loads are placed on the robot, how slippery the oors are, or what the actual charge is of the batteries, as long as they are within some normal operating ranges.

An agent needs to do qualitative reasoning to determine which quantitative laws are applicable. For example, if the delivery robot is lling a coffee cup, different quantitative formulas are appropriate to determine where the coffee goes when the coffee pot is not tilted enough for coffee to come out, when coffee comes out into a non-full cup, and when the coffee cup is full and the coffee is soaking into the carpet. Qualitative reasoning uses discrete values, which can take a number of forms: Landmarks are values that make qualitative distinctions in the individual being modeled.

In the coffee example, some important qualitative distinctions include whether the coffee cup is empty, partially full, or full. These landmark values are all that is needed to predict what happens if the cup is tipped upside down or if coffee is poured into the cup. Orders-of-magnitude reasoning involves approximate reasoning that ignores minor distinctions.

For example, a partially full coffee cup may be full enough to deliver, half empty, or nearly empty. These fuzzy terms have ill-de ned borders. Some relationship exists between the actual amount of coffee in the cup and the qualitative description, but there may not be strict numerical divisors.

Qualitative derivatives indicate whether some value is increasing, decreasing, or staying the same. A exible agent needs to do qualitative reasoning before it does quantitative reasoning. Sometimes qualitative reasoning is all that is needed.

Thus, an agent does not always need to do quantitative reasoning, but sometimes it needs to do both qualitative and quantitative reasoning..
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