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Computer programs are full of code that makes use of Boolean logic. If this condition is true AND that condition is true, perform this action. The basic Boolean operators are AND, OR, and NOT, though the exclusive-or XOR is also popular. The Boolean operators take one or more inputs and return a single output value. The inputs and outputs are all True/False values, Boolean binary. We use 0 for false and 1 for true.
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The easiest way to describe the operators is with a truth table. Table 17-1 shows this for the basic Boolean operators. For example, the AND operator only returns true if all of its inputs are true. OR returns true if any of its inputs are true. Complicated conditions can be built up, specified by a truth table. Any arbitrary truth table can then be decomposed to an equation using the basic operators of AND, OR, and NOT. More interesting results can be found using predicate logic. Using predicate logic, and its various relatives, you can represent facts in the computer s memory. From these facts, the computer can infer new facts or find new relationships between existing facts. For example, given these assumptions: Rabbits are faster than turtles Turtles are faster than snails Robert is a rabbit Steve is a snail You could ask the system if Steve is faster than Robert and it would answer no. Nowhere is there a rule that says Robert is faster than Steve, or even that Rabbits are faster than Snails. The information provided is su cient to gure out the answer. Logical programming was the basis of artificial intelligence when the field first began, and it still has lots of value in expert systems and some other formal information-processing fields. It doesn t relate directly to robot control and it doesn t really represent how people think.
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Artificial intelligence researchers and robot builders alike have been looking to nature for inspiration on how to build their systems. All of the animals have successful control systems, providing quick reactions to a changing environment as well as high-level planning and guidance the thinking meat of Terry Bisson s short skit, They re Made of Meat:
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... Oh, there is a brain all right. It s just that the brain is made out of meat! So what does the thinking You re not understanding, are you The brain does the thinking. The meat. Thinking meat! You re asking me to believe in thinking meat! ...
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Fortunately for us, our thinking meat serves us well.
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CHAPTER 17 Intelligent Behavior
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Table 17-1 Boolean operators AND A 0 0 1 1 B 0 1 0 1 OR A 0 0 1 1 B 0 1 0 1 XOR A 0 0 1 1 B 0 1 0 1 NOT A 0 1 Out 1 0 Out 0 1 1 0 Out 0 1 1 1 Out 0 0 0 1
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Intelligent Behavior
The lowly neuron is at the core of all of the brains that we have examined so far. The neuron is like a little computer itself, though it seems to perform fairly simple computations. A bunch of neurons together, a neural network, appears to work magic. While we don t know how the human, or any other, brain works in detail, that doesn t keep us from using what we do know to design more effective software. One of the things that brains do best is to make sense of incomplete information. A form of pattern matching finding the pattern behind noisy and incomplete data. Recognizing faces. Those tasks that traditional computer software finds really difficult, brains do with ease.
Pattern Recognition
One of the common problems noted in earlier sections was the problem of knowing when a task is done. This is a pattern matching or pattern recognition problem. Have you found the right person Are you in the right part of the room Has the robot s arm reached the target yet There are many approaches to pattern matching. For simple sensors a trivial test may be sufficient. Is the reflected light below 30 Normally, though, there will be more than one sensor. There is often more than one value associated with a single sensor. Cameras, for example, return thousands if not millions of values, the individual pixels in their image.
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