Starcraft AI blog

I am bitterly disappointed. I wrote my opponent modeling code. I kept it as simple as I could, since it’s the first draft of a major new feature. When it seemed to work I hooked it into the strategy boss, which then made decisions based on the enemy’s predicted future unit mix, not based on the current observed unit mix. I ran a few test games without visible problems. Then I tested more thoroughly, and turned up severe bugs. It is not close to solid enough to enter into a major tournament. And as far as I am concerned, frantic last minute debugging is not a plan.

I got what I deserved for hurrying to meet the deadline. I should have run careful tests earlier. I still think I had enough time to polish up the basic features, if I had only worked wisely. It’s not that complicated. It’s a simple idea that nobody happens to have implemented before (that I know of).

Anyway, for the AIIDE version I’ll turn off opponent modeling and remove the hookup into the strategy boss. I’ll release it as version 1.3.1, with a configuration file specially tuned for the tournament. It does have bug fixes and other improvements over the current version 1.3, so it should play a little better. (I count 19 bug fixes and small improvements plus 1 minor new feature, as well as new 2 hatch and 3 hatch lurker openings.) Then I’ll take my time with opponent modeling and release version 1.4 when it is good and ready.

The opponent model has strong capabilities in principle. It can meet most of the goals I laid out. If there is data—enough data from past games and enough from the current game—it can estimate the enemy unit mix at any future time during the game, and can predict the times of important events like “enemy gets air units” and “enemy gets detection.” An opponent that plays a fixed strategy should turn transparent by the second game. An opponent with a modest number of fixed strategies should become predictable as soon as Steamhammer has seen all the strategies—and when the current game has progressed far enough to distinguish them. I do expect trouble predicting opponents which successfully hide their tech, or which make important decisions in the middle game randomly, or which (like Steamhammer itself) play a wide range of strategies.

The downside is that it is only a data structure. Each use of the prediction capability has to be coded into the bot separately. I coded the prediction routine to estimate the enemy’s future unit mix. I coded 2 uses of the prediction in the strategy boss, one to set the tech target and one to set the unit mix (using different time horizons). When I want to use predictions to make spore colonies in time, or to get the right number of scourge at the right time to counter XIMP’s carriers as they show themselves, I’ll have to separately code more uses into the strategy boss, because of the scattered way the strategy boss works. When I want to use it for opening selection, I’ll have to code a different prediction routine and use it. And so on. The ability to predict is powerful, and you have to pay for the power.

Since I kept things simple for the first cut, predictions may be a little unsteady. It should be good enough to be useful, but whether it is or not, there is room for improvement.

I’m bitterly disappointed not to get opponent modeling into AIIDE. It’s an easy idea—to me it’s obvious, and I have to imagine that nobody else has done it only because they’re concentrating on other aspects. But it’s also new and I expect it to be successful. If I’m right, then other authors will soon be borrowing my opponent modeling code or writing their own along similar lines. The actively developed bots may take a step up in ability.