Complete AI System for Torque

Well, it's done. I've finally finished my ai system, except for a few minor things. (Well, I actually finished it a while ago, but I've been too busy to post it up here.) My basic goals in this project have been to make an opponent ai that is flexible, extensible, and, once completed, easy to implement. This has probably been one of my most ambitious coding projects yet, and I'm glad to say, I'm quite satisfied with the results. It uses a three-layered system: At the top layer, a genetic algorithm causes the bot population as a whole to learn and adapt over time. In the middle layer, a each bot uses a neural net to evaluate relevant input information and make a decision as to what to do next. And at the bottom layer, an A* pathfinder tells the bots how to get where they want to go.

Now down to the nitty-gritty description. When the game starts, the AIManager is created. It loads the neural net gene pool from a file, or creates a blank pool. (It has to be randomized from the console because of certain annoyingnesses with global object constructors.) When an AIPlayer "bot" is created, the AIManager combines two random nets from the better half of the gene pool, and gives it to the bot.

Then, the thinking begins. Each bot's thinking function is called, every other tick, by the AIManager. (I'm considering, for efficiency's sake, doing this some other way.) The bot converts all the information is uses into values from -1 to 1. There are currently 7 inputs: Whether the bot is doing anything, how long it's been since it last saw the player, its health, the player's health, how far away the player is, what it's doing, and one blank input that's there for a very good reason that I forget. This information is fed through a feed-forward neural net. There are five outputs, and the bot uses the highest of the five to decide where it wants to go.

Now the A* part begins. Back when the map was loaded, another global object, AStar, used a bunch of special NavMesh SceneObjects to create a grid of navigation points spanning the map. I have a good picture of this in one of my previous blog posts (a picture is worth a thousand subclauses). When the bot decides where to go, it calls one of five functions. They are stopMove(), findPathTo(), sneakUpOn(), findCoverFrom(), and wander(). The AStar object uses variations on the classic a* algorithm, and returns a list of points for the bot to follow. The bots use the original AIPlayer path following code, however keeping track of paths has been moved to c++ for simplicity's sake.

Each time the bot inflicts damage on the player, the bot's fitness score is incremented. This is how the effectiveness of each net is kept track of. When the bot dies, the AIManager::removeBot() function is called. (and a string of passive voice sentences are written) This replaces the lowest scoring net in the gene pool with the bot's net. The gene pool is then saved back to a file.

Read it again, it might make more sense this time.