Starcraft AI blog

I’ve been testing out Steamhammer’s new opening selection algorithm, part of the opponent model.

CasiaBot plays a hand-made anti-Steamhammer build, sunken turtle into fast spire. (It does this 80% of the time, and 9 pool speed the other 20%. It’s hard to counter both at once.) Steamhammer’s strategy reactions to it are in the right direction, but are too slow and sloppy. For SSCAIT 2017, I hand-made an anti-CasiaBot build that holds off the mutalisks with a slightly faster spire and gets a second gas to win with numbers, while preventing CasiaBot from ever expanding. CasiaBot’s build is hard for Steamhammer to beat, much tougher than CherryPi’s similar turtle build. But the counter usually works, and Steamhammer scored 2-0 in the round robin.

CasiaBot was a simple first test. In the first game, Steamhammer made its usual random opening selection and lost to the turtle build. But the plan recognizer recognized the build. In the second game, Steamhammer played its counter and won. The system worked.

I didn’t test against CherryPi, but I would expect this sequence as the most likely: Steamhammer wins the first game, because its random opening selection usually beats CherryPi’s first ZvZ opening. CherryPi switches to the turtle build for the second game and evens it at 1-1. (That’s what happened in the round robin.) Steamhammer switches to the counter-turtle build and pulls ahead again to 2-1. I don’t know what happens after that. CherryPi might have another winning build and even it again at 2-2, which would set up a learning race.

UAlbertaBot is a much tougher test because it plays random, and Steamhammer doesn’t have the smarts to fully adapt to each of the strategies UAlbertaBot follows. My hand-made counter scores about 2/3, almost always winning against zerg, mostly winning against terran, and usually losing against the protoss zealot rush.

I turned off the hand-made answer and let Steamhammer learn. In the first game, UAlbertaBot rolled terran and lost to Steamhammer’s random choice. In the second game, UAlbertaBot was protoss and Steamhammer played a counter to the terran opening it had seen: Steamhammer doesn’t need to lose to learn. Stopping the marines and stopping the zealots can be done in a similar way, and Steamhammer won again. Then UAlbertaBot rolled zerg twice in a row, and Steamhammer mis-countered both times. In game 5 UAlbertaBot was protoss again, while Steamhammer (having been hit with 5 pool twice in a row) thought its opponent had switched to fast rushes and played to stop the early zerglings, losing again. But at the end of 15 games the score was 9-6, not distinguishable from the 2/3 winning rate of my hand-made counter. The openings played were completely different, most wins were against protoss and terran instead of zerg and terran, and the reasoning behind the choices was the practical “this is what I saw, now beat it” instead of the theoretical “this is what ought to work” of my hand-made counter, but the end result was the same. Good enough!

Only longer experience will show how well the system works in the wider world of the SSCAIT ladder, and whether it can hold up when bot authors look for and exploit its weaknesses. I have thought of a lot of ways it might break down. I know it doesn’t work against Juno by Yuanheng Zhu. But I have also thought of a lot of ways to improve it. I have ideas in mind for the plan recognizer, the opening selector itself, and the reactions to recognized plans. A lot of information is not exploited yet. Some ideas I will get to before release, some will wait for later versions in the 1.4.x series, and some will wait a long time.

Near the 1.4 release I’ll write a description of how it works, up to date with the release. There are several working parts, but for what it does, it’s not complex. It acts for all 3 races, retains configurability, allows for random choice of openings in every situation if the author wants, and expects that some opponents will change their behavior over time. I imagine that getting all these features is simpler than you expect.