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TRUE INTELLIGENCE: ADAPTIVE BEHAVIOR
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the robot will learn how make its way through the environment to satisfy its need. The robot s behavior will make you think that a path has been stored in its memory, but this is not the case. The movement along the apparent path takes place based on individual associations not on a single memory of the entire path. This algorithm is not unlike the way memory associations are formed in biological creatures including humans. Our algorithm is much simpler than a human s (it does not include value judgments) but the nished program demonstrates that the algorithm does in fact produce intelligent behavior. The use of cell numbers is just an easy way for the robot to recognize where it is. Living creatures use senses such as vision and smell to link their memories to situations and places. Also we did not implement the opportunistic behavior discussed earlier (also see Exercise 5). 16.4.3.4 DoMovement This module starts by displaying the current memory sizes on the screen and then saving the current position (CurCell) into the variable LastCell so that it knows where it was before the movement is made. The internal needs (hunger, sleep, and boredom) are increased appropriately and displayed on the screen meters. The compass is used to turn the robot in the proper direction before the robot tries to move to the next area of the screen (the robot s environment is actually divided into 30 regions or cells. Each cell is given a numeric value and the current cell s number is calculated from a formula and stored in the variables Rx and Ry, which keep track of the robot s cell position). If the robot bumps into something while moving, it sets Status to PAIN so that it will learn not to do that movement again while in this cell. The robot automatically moves back to its original position when it encounters a painful situation. Similar actions occur when the robot moves into a briar patch or too close to the re. The last thing that happens in DoMovement is a call to CheckStatus, which causes the robot to react based on its status. Essentially, the robot will eat if it is hungry and in an area of food, sleep if it is sleepy and in the sleep area, and play if it is bored and in the play area. During these processes, other needs are increased reasonably. If you eat, for example, it tends to make you sleepy. If you modify the parameters in this routine you can affect the personality your robot develops. A major contributor to the robot s personality is the situations it encounters as it matures (just like living creatures).
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16.5 Summary
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In this chapter you have: Seen how adaptive behavior can be more effective in tackling a wider variety of unpredictably changing environments. Been exposed to the basic principles for creating adaptive control algorithms. Seen an example program that attempts to use the robot to model the behavior of biological organisms. Been shown that even an arti cially intelligent robot can develop a distinct personality and unique habits based on how it grows and develops in its environment. Now, try to do the exercises in the next section.
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GOING FURTHER
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16.6 Exercises
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1. Run the program in this chapter to see how it performs. Examine the code and try to
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change some of the biological characteristics to see how they affect the robots eventual personality and habits. Modify the program so the robot will try to move toward the mouse pointer when it is exploring its environment. Help the robot learn faster by using the mouse to entice the robot to move toward food, activity, and sleep areas when a need is active. Compare this activity with teaching young children new things. Modify some of the earlier programs in this book (such as, following a line or following a wall) so that the algorithm includes some form of adaptive behavior. Compare the performance of your programs with the original ones. In the algorithm of this chapter, once an item was added to memory it stayed there permanently. The robot cannot forget. To make the robot behave more like a biological creature, it has to be made to forget. Implement a forgetting procedure to occasionally delete the last entry (or perhaps a random entry) in the memory arrays on a random basis. A more complicated, but more realistic simulation would be to have a long-term memory and a short-term memory and have the robot transfer some (not all) the short-term memory into long-term memory while sleeping. Also you may want some mechanism for enforcing short-term memory the more times a situation is encountered, and give preference to these reinforced items when transferring to longterm memory. In the algorithm discussed in this chapter, the robot was only motivated by a single need based on biological priorities and thresholds. In other words, the robot does not develop complex value judgments. This can be observed when the robot fails to play when it enters the play area if hunger is the current motivator. Imagine how this behavior could be altered so as to be more opportunistic. The robot could decide to take advantage of being in the area and play now even though it is hungry. Some internal mechanism (perhaps opportunity thresholds) must control this behavior. The robot should not play if there is not a reasonable need to do so. Likewise, the robot should forego play and head for food if the hunger need is very large. Modify the algorithm in this chapter to implement such an adaptation.
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