Starcraft AI blog

Thanks to SCHNAIL we’re starting to see bot matches against strong human players. Today I write about the Cadenzie (Z) 5 game match versus Locutus from last Tuesday. It comes with an interview on Making Computer Do Things. Watch the games first if you’re interested.

Cadenzie is not a top pro; to me her play looks a little slow and awkward compared to the best. But she is very strong, and when the match was announced, I expected her to win every game. In the event, she scored 3-2. I thought all the games were one-sided: Either zerg won fairly easily, or else Locutus collected enough dragoons and was able to overpower her hydralisks with superhuman dragoon micro. In the game where Locutus chose to go zealots instead, the zealots looked wimpy; Steamhammer has had the same experience.

I also felt that the first game was the only one that Cadenzie played with 100% seriousness (and she said as much in the interview). She played a well-rehearsed build with mass hydras and drop. Locutus (lacking PurpleWave’s strategy skills) did not understand how to read her build in order to cut corners in the opening, and it fell behind (slightly behind in bot terms, “massively” behind according to Cadenzie—the distance varies by skill level!). When hydras collected outside its natural, Locutus trickled units out through the narrow opening in its wall and let them be picked off, falling further behind. The front gateway fell, and then overlord speed finished. She’d gotten overlord drop first, and she picked up hydras and put them in the protoss main, where they cleaned up with little effort. I judged that she could as easily have skipped drop and powered through the front door.

I thought the most interesting answer in the interview was “I played in a tournament before where there was a team melee relay style with a mix of progamers and beginner level players and they would take turns every 2 minutes, in a way it was most similar to that.” In other words, Locutus was extremely good at some aspects of the game, and extremely weak at others. That is similar to other games where computer programs were good enough to play humans and not good enough to win every time; for example, chess programs in the old days were superhuman at tactics and weak at strategy in a very similar way.

She repeatedly emphasized that bots need to adapt more to what they scout. I think that’s the main takeaway.

Compare Artosis versus top bots on Twitch: Notice how often Artosis says “In this build, when I see such-and-such, I do so-and-so.” Human players have extensive knowledge of how to play in specific situations, and no bot comes close. SAIDA may come closest, with its one all-purpose build and numerous reactions, but its understanding is shallow by comparison. PurpleWave I think has the greatest strategy knowledge of any bot, but it has a weak understanding of tactics. Locutus relies on its strong micro and aggressive tactics, which cover for the weaknesses in other aspects. Bots not only know less, they don’t integrate their knowledge into a theory of how the game works—they don’t understand what they know, so they a weak at drawing inferences and their adaptation ability is shallow. See for example the Artosis game versus Killerbot by Marian Devecka, where Artosis was able to guess that a third zerg base at an early timing was likely, and scouted for it specifically. In other games, he did not spend effort scouting for expansions he did not see as likely.

By the way, the last game is the best, Artosis vs McRave starting at ~2 hours in.

Filling the knowledge gap I believe will require machine learning. Writing rules and reactions by hand will take a day or two less than forever, and search will not solve all problems if it has only handmade evaluations to rely on. For Steamhammer, I’ve figured out a way to put together familiar algorithms that will execute fast, and I expect it will also learn fast (from little data) and be reasonably accurate (not amazing like deep learning, but adequate). It’s part of my strategy adaptation goal. If it works as well as I hope, I WILL CRUSH YOU PUNY MORTALS BENEATH MY STEEL THUMB BWAHAHAHAHA, or something like that. Actually the first application will be nothing more than an evaluation function to choose openings and strategies, valuable but extracting only a little of the potential, and if it’s successful then after I explain how it works everyone else will get ahead of me again. That will be good too.