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GOING FURTHER
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One way to detect if a line is curving is to equip the robot with more than three line sensors [see Sec. C.9 for details on rGroundA()]. When following a relatively straight line, only sensors near the robot s current heading would detect the line. As the line curves, the outer sensors start to trigger. We need to specify how the robot should react when the outer sensors are triggered. There are many options for how we can make the robot react. One possibility is to make the robot turn more sharply. Another is to make the robot slowdown. Instead of specifying what the robot should do when the line curves, imagine a robot that can decide on its own how much to turn when it is in such situations. Previously, we simply guessed the required turn amount when sensor data showed that the robot was veering from the line. If the guess was too high the robot turned too much and lost the line. If the guess was too low the robot was not able to stay on the line when the line turned sharply. Our solution has been to test the program on a typical line from the expected environment and manually adjust the amount of turn until the robot performs satisfactorily. Instead of programming the robot to turn 2 , for example, we could tell it to turn TurnAmount degrees where TurnAmount is a variable. The robot could be programmed to automatically try different values for TurnAmount and see what happens. The robot would have to be able to detect when it loses the line and then adjust TurnAmount and try again. Once the robot nds an acceptable value for TurnAmount it would use this value from then on, but this concept can be improved further. We can program the robot to continually alter the value of TurnAmount based on its situation. Of course, a robot that can, in essence, program itself, needs a way to evaluate its own performance. This means that the robot must be given the means to determine when it has lost the line, and a way to nd the line again so that it can try again with the adjusted parameters.
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16.2 How to De ne Intelligence
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Robots that adapt their behavioral rules and parameters are de nitely more intelligent than those that behave in a predetermined manner. The question is, are they truly intelligent The answer depends on how you de ne intelligence. Most people would argue that the robot is not truly intelligent because it is not making decisions the way human beings make them. 16.2.1 HUMAN INTELLIGENCE Many factors affect how humans make decisions. Certainly memories affect the decision process. If an action causes pain it is less likely to be repeated in the future. On the other hand, actions that create pleasurable outcomes are more likely to be repeated. The environment also affects the decision-making process. At the very least, the environment limits the range of choices. Even the food you eat affects your actions. It is obvious that normal body chemistry would make you less likely to eat something sweet if you have just eaten a large bowl of ice-cream, but other effects might be less obvious.
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TRUE INTELLIGENCE: ADAPTIVE BEHAVIOR
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Sugar in your blood stream, for example, might make you choose to nap instead of exercising. 16.2.2 INTELLIGENCE THROUGH ASSOCIATION Actions that create pleasurable outcomes are more likely to be repeated. One question that should come to mind is how do humans determine what is pleasurable. Certainly, human biology imposes many factors. All newborn babies nd cold and hunger an unpleasant experience. Conversely, food and warmth are deemed pleasurable. The brain commits to memory many associations with these biological factors as a baby grows to adulthood. There are also indirect associations. If a mother provides warmth and food for her baby, she will be associated with pleasure and thus is placed on the baby s good list. It is not hard to imagine that things that are associated with the mother would also be considered pleasurable. The behavior described above is deceptively simple, yet amazingly effective. In general, it means that a baby learns to achieve pleasure not only from things that directly give pleasure, but also from things that are associated with things that give pleasure. These associations, along with the current environmental conditions, control our behaviors. Early associations are straightforward but as the baby matures, creating the lists becomes more complicated. New situations and actions are often associated with things on both the good and bad lists. This means that many situations are not interpreted as strictly pleasurable or painful. Consequently, future choices are not just black or white, right or wrong. When our brain tries to analyze the choices of this nature we refer to the process as making a value-judgment. The diagram in Fig. 16.3 shows a feedback loop that depicts how humans react and behave. Notice the loop is not really that different from the one in Fig. 16.2. The real difference is in the way that memory affects the sensory comparison process. Human perception is affected by memory as well as by the actual state of the environment and sensory organs. This is why we often perceive erroneously even when we sense correctly. This concept also explains why some people make some poor decisions in life.
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