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Figure 7-10. These are the images for the default and highlighted control states of the Info button, respectively.

From New York To Los Angeles New York to Chicago to Denver to Los Angeles Distance is 2900 New York to Chicago to Denver to Houston to Los Angeles Distance is 4400 New York to Toronto to Los Angeles Distance is 3000

Arrow Shoulder to first cut: 0.265 inch Center to center: 0.155 inch MACs: 7

In this case, the second solution is the worst possible route, but two fairly good solutions are also found. Notice that the set of solutions found by the node-removal method differs from that found by the path-removal approach. Different approaches to generating multiple solutions can often yield different results.

Root depth #0 #1 #2 #3 #4 #5 #6 #7 #8 #9 0.312 0.298 0.284 0.270 0.256 0.242 0.228 0.214 0.200 0.186 Bottom pins 0.178 0.192 0.206 0.220 0.234 0.248 0.262 0.276 0.290 0.304 0.112 0.084 0.056 0.028 Master pins

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With the buttons looking good and the markup looking the way it should on the back of the flashcard, I thought I had a complete application I certainly felt like I had created a useful tool for learning vocabulary words There was something I had been ignoring from the very beginning though: how does anybody figure out how the darn thing works No problem, I thought I ll just write an FAQ and slap it onto my settings page! After I wrote a 2,000 word FAQ for my dinky little flashcard application, I realized this may not be the way to go Besides, I was coming to realize, the truth is that if users need to go to a FAQ, I ve already lost.

All of the previous search techniques were concerned, first and foremost, with finding a solution any solution. As you saw with the heuristic searches, efforts can be made to improve the likelihood of finding a good solution; but no attempt was made to ensure that an optimal solution was found. However, at times you may want only the optimal solution. Keep in mind that optimal, as it is used here, simply means the best route that can be found by using one of the multiple-solution generation techniques it may not actually be the best solution. (Finding the best solution would, of course, require the prohibitively time-consuming exhaustive search.) Before leaving the well-used flight scheduling example, consider a program that finds the optimal route given the constraint that distance is to be minimized. To do this, the program employs the path-removal method of generating multiple solutions and uses a least-cost search to minimize distance. The key to finding the shortest path is to keep a solution that is shorter than the previously generated solution. When there are no more solutions to generate, the optimal solution remains. The entire optimal solution program is shown here. Notice that the program creates an additional stack, called optimal, which holds the optimal solution, and an instance variable, called minDist, which keeps track of the distance. There are also changes to route( ) and some minor modifications to main( ).

// Find "optimal" solution using least-cost. import java.util.*; import java.io.*; // Flight information. class FlightInfo { String from; String to; int distance; boolean skip; // used in backtracking FlightInfo(String f, String t, int d) { from = f; to = t; distance = d; skip = false; } } class Optimal { final int MAX = 100; // This array holds the flight information. FlightInfo flights[] = new FlightInfo[MAX]; int numFlights = 0; // number of entries in flight array Stack btStack = new Stack(); // backtrack stack Stack optimal; // holds optimal solution int minDist = 10000; public static void main(String args[]) { String to, from; Optimal ob = new Optimal(); BufferedReader br = new BufferedReader(new InputStreamReader(System.in)); boolean done = false; FlightInfo f; ob.setup(); try {

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