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The example of learning a map, above, is an instance of throwing the robot into an environment and expecting it to learn without any supervision. It is easier on the AI, if not for you, to provide a bit more direction.
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Fig. 18-6.
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Supervision can be applied to the mapping problem. The robot is given a map that is already lled in and it tries to keep track of its position on this map. The odometry methods place the robot on the map as it wanders across the center. Then, when the robot senses a wall it can compare its sensory input to the map and adjust its concept of where it is on the map. This combines the data from the odometry system with sensory data and applies it against the template of the built-in map. If the robot reaches a corner, it can perform an even more accurate realignment of its internal map, since the corner constrains the robot s position along two axes of motion. Many arti cial intelligence technologies use a form of supervised learning. Supervised learning provides both the question and an answer, and the system learns the connection between them. We apply a training pattern to patternmatching neurons in Fig. 18-7. In this type of supervised training, the programmer sends an input pattern xn to the neurons as well as the desired output Tm, or training pattern, upstream to the outputs ym. The input is processed and the actual results are compared to the desired output. The di erence, or error, is propagated
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Fig. 18-7.
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Supervised training of pattern-matching neurons.
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backward through the neurons. This error propagation changes the values in the neuron s templates so they are more likely to generate the desired output for this input. Over time, and with a reasonable set of training patterns, each neuron in the network will come to recognize a di erent distinct input pattern. Inputs that are near, but not quite the same, as a known pattern will activate a neuron with a similar pattern more than a neuron with a distant pattern. This simple recognition system is known as a perceptron, and it was one of the rst neural network models. This one-layer network is a good start, but it is limited in how well it can abstract information. For example, it was proven to be unable to learn the XOR function. This limitation was so crippling that neural networks were abandoned as a technology for a long time, until some new insights were developed. By adding another layer, the network can learn complex associations between input and output (Fig. 18-8). This multilayer perceptron (MLP) is a truly useful processing module. Training is still supervised, and the neural template adjustments are still done automatically. Where information in our map was stored in a regular grid, information in these networks is stored in the template in each neuron. In Fig. 18-7, these templates even made sense. Moving into Fig. 18-8, the values matched by the neurons are less straightforward. One of the drawbacks to multilayer neural
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CHAPTER 18 Advanced Control
Fig. 18-8. Multilayer perceptron.
networks is that you can t really pry the top o of the system and see what it is doing. The numbers the network learns don t necessarily make sense by themselves, but work together to create a useful answer. In a sense, each piece of information is stored across the entire network. Some authors have even compared the brain s method of storage to a hologram.
SUPERVISED ROBOTIC LEARNING
Assuming you have a working neural network, how does this apply to your robot The input pattern could be values from a number of sensors on the robot. The output is then the control signals that drive the robot. This is a re exive type of intelligence that can be used for low-level control. You can train this type of system to perform simple tasks. Perhaps the easiest way to train it is to show it how to perform the task. The trainable robot is a combination of self-guided machine and telerobot. In training mode, a human operator controls the robot s actions, guiding it through the task and around the environment. The neural network watches as the operator guides the robot. It matches the sensory inputs with the control signals sent by the human operator. When turned loose on its own, the sensory signals stimulate the neural net, which generates control signals that are similar to the human s control during training.
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