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Here s another interesting problem that s solvable with A*. Imagine that you have enemy AI characters that are using A* to find the best exit to a dungeon. The only problem is that you ve discovered that you can easily rack up a high score by hiding near that exit and knocking off each enemy as it blindly stumbles by. The enemies have no way of warning their friends that this might be the shortest route but it s also extremely dangerous. You can fix this by using what s called an influence map. If an area of the game world becomes particularly dangerous, make those nodes very high cost. When A* searches for a path, it will avoid those expensive, dangerous areas. You can also extend this concept to solve the problem of many enemies following the same path. In many games, it will seem very unnatural if all the enemies choose the same shortest path. You can force enemies to take a different path by tracking the path that each chooses, and assigning high costs to those nodes. A* will then avoid nodes and paths that have already been chosen by other enemies.
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Earlier, I mentioned that A* was an improvement over Dijkstra s algorithm. This is a bit misleading because, in fact, Dijkstra s algorithm is better to use when you don t know the final destination of the path. The only difference between A* and Dijkstra s algorithm is that A* adds heuristics. In Dijkstra s algorithm, H always has a value of zero. The means that when Dijkstra s algorithm starts looking for a path, it doesn t know in which direction to start looking. It must do a lot more searching than A* to find the goal. But what if you have a game where you re not sure where the character s final destination will be Imagine that you re designing a strategy or resource management game and your villagers need to collect strawberries. There are four strawberry bushes around the town, but you don t know which bush is the closest. If you use Dijkstra s algorithm, it will search outward in all directions until it finds the first one. If you use A*, you will need to calculate four different paths to each bush and choose the shortest. Dijkstra s algorithm saves you from having to do this, so it would be a better choice in this case. If you want to use Dijkstra s algorithm rather than A*, just assign 0 to all the H costs. The rest of the A* code will be the same.
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This chapter has presented a brief introduction to pathfinding, which should get you thinking about what s possible for your own games. Adventure games, strategy games, and any games that require sophisticated AI will benefit from these techniques. In 10, we ll finish the book by taking a look at how to work with external game data. Saving games, loading games, and using XML are coming up next.
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You might be surprised to know this, but we ve just about covered all the most essential topics in game design. We ve left few stones unturned, and I hope you ve been inspired to build a few new games along the way. In this last chapter of the book, we re going to look at how to load and save game data using some of AS3.0 s built-in tools. We ll cover these topics: Local shared objects Loading and saving files using FileReference Using XML to store and load game levels Loading files at runtime using URLLoader Flash Player security issues These are very general techniques that have a wide application in games. You can use them along with any of the other techniques we ve covered in this book. At the end of this chapter, I ll give you a few suggestions on where to go from here to take your study of game design further.
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