Starcraft AI blog

My time and energy are still limited. Well, my plans always outrun my execution anyway. Here are things I hope to get to soon:

• Write up a confused Steamhammer-McRave game.
• Look into AITP’s code.
• I forgot to release source for Steamhammer 2.3.6. I should do that.
• Look more closely at ZZZKBot’s wide swing in AIIDE results.
• Analyze CoG and the AIIDE unknown maps competitions to some degree.

For Steamhammer, I’m constantly torn between the need for immediate repairs of glaring weaknesses and the need to make long-term progress on strategy adaptation and infrastructure rework. The long run is what counts, but there are always lost games and upcoming tournaments to urge me to make quick fixes.

I will do both. For the upcoming SSCAIT, it looks like I will make another point release, Steamhammer 2.4, in which the visible changes will be more on the quick fix side. It will have work (it already has work) on strategy adaptation, but little or none that will be active yet. I don’t have the energy to put my head down and bring off a substantial subproject.

Old zerg bot StyxZ has been getting updates recently, and I have been watching it steadily climb the rankings. Today Styx became the top zerg on BASIL, at least for the moment, so clearly it is time to write about it!

To give an impression of how far Styx has climbed, its lifetime win rate on SSCAIT is about 34%. In the last 24 hours on BASIL, I count 5 losses and 35 wins, an 88% win rate. On the BASIL crosstable its row is almost all red and stands out sharply in its new green environment. The degree of improvement is astounding.

Styx is distributed as a JAR file, so I unpacked it and took a look. I found that Styx is written in Kotlin, a recent language. That makes me wonder: Is this Styx version a complete rewrite, new code that replaces the older version? That would fit in with the quantum leap in results. But I also see both of the map analysis libraries BWTA and BWEM, which gives the impression that Styx is in the process of transitioning to BWEM, though perhaps it is using both to take advantage of different features. Also in there is the jts geometry library, which seems potentially useful for map-related calculations. The jts version is 1.16.1, officially released this past February (I love that it is the same as our Starcraft version number).

Also in the object code I see the ASS combat simulator, a project started only last year. I see the jsoniter JSON parser, which suggests that Styx is reading data files of some kind, whether static initialization and configuration or dynamic learning files.

The feeling I get is that work on Styx was restarted sometime this year, and a substantial amount of new work has been done. It’s hard to say exactly what, though, since I didn’t save an old version to compare.

What specifically makes Styx stronger? The most obvious improvement is a new build, much sharper than what Styx used to play: It is 9 pool, second hatchery, then zergling speed, and don’t waste time making extra drones but keep up zergling production with a 7 drone economy, which enough for constant production from the 2 hatcheries. The mass lings overrun opponents of every race, including solid defenders like McRave. The purple zerg PurpleSwarm has been playing a similar build for a while, also with success.

The new build is great, but it does make me wonder how much the other changes to Styx contribute.

Adias is a new terran bot, and starting off strongly. The first thing I noticed about it is that its name is SAIDA spelled backward. Does that mean it is like SAIDA, or the opposite of SAIDA? The second thing I noticed, after unpacking the binary from SSCAIT, is a machine learning folder with files caffe2.dll, torch.dll, and a couple of learned models with the .pt suffix. It is the signature of a project using PyTorch for deep learning. It’s not TorchCraft, so not directly related to CherryPi. It also does not seem to be based on SAIDA_RL; strings I checked from SAIDA_RL do not appear in adias.

The third thing I noticed, peering into the .exe, is that it looks to be derived from SAIDA. It’s clear that the two share a lot of code, at least. That’s surprising; based on SAIDA and using RL but not based on SAIDA_RL? Close similarities to SAIDA are visible in the game play. For example, adias shares SAIDA’s way of grouping supply depots and tech buildings together, and SAIDA’s predilection for walls composed of barracks plus engineering bay. Both habits appear in the game adias-Tomas Cere on Benzene. There are a lot more similarities; the building placement and opening build orders look identical to me, and adias shares the skill to set up tank lines where the opponent will be compelled to engage at a disadvantage.

The combination of traits reminds me of something else: Stormbreaker, the bot which was disqualified from AIIDE 2019 for being SAIDA with cosmetic changes and a neural network whose result was not used. Is adias only Stormbreaker under a different name?

Well, I don’t know, but I’m guessing not. Though SAIDA and adias look more similar than not, I went back and watched old SAIDA games and I think I see differences too. The difference most salient to me is that adias likes to send an SCV to scout around inside and near its base early in the game, seeming to check for proxies. The only other bot I’ve seen with that style of anti-proxy scouting is tscmoo. Did tscmoo have a hand in adias?

I expect we’ll learn more as we get to see more games. Krasi0 was eventually updated to defeat SAIDA. Will Krasi0 quickly learn to beat adias too, or will Krasi0 have to be updated again to keep up?

I think AITP is the only AIIDE 2019 bot whose game play deserves its own post. Most others can be watched at SSCAIT and BASIL, and BunkerBoxeR is severely buggy. AITP finished second to last, but it is complex and interesting. In this post, the game links are links to replays; you’ll have to drop them into the OpenBW player yourself.

AITP is derived from Steamhammer and shares some of the same habits. For example, it places supply depots and other buildings of the same size preferentially at the edge of the map, like Steamhammer terran. As explained in what AITP learned, the bot has an interesting abstract strategy system that is different from any other Steamhammer derivative.

AITP does not have strategy work only. It knows how to make a wall at the ramp, how to lift the barracks to open the wall, and how to land the barracks nearby to leave the wall open. It also knows how to lift the barracks back and restore the wall in case of danger. It researches spider mines and lays mines in neat diagonal lines in places like the approach to its natural. Later in the game, when it has a dangerous tank ball, it moves out and sieges to cover movement routes, like SAIDA but with less understanding of where the important places are, and builds turrets there. It has an excessive desire to build bunkers in the middle of the map, which it then doesn’t use properly.

It has also lost important skills that it inherited. It seems to have lost the emergency reaction of taking SCVs off gas when mineral mining is more important because of a large gas excess. Marines may stand away from a bunker that they should jump into, a new and critical misplay. AITP often moves its army away from the action toward a distant empty base, as in this game versus XiaoYi on Fortress (possibly a bug in deciding on the squad target).

Overall, I judge AITP as promising but not mature. I imagine that the tournament came up too early in its development. Some of its skills are impressive, others look incomplete or broken. It needs work to fix bugs (223 crashes during the tournament), polish skills, and add strategy modules to cover more cases. If it gets that work, I think it could become very strong.

Here are my observations of how AITP played when following each of its 5 declared openings. I correlated its learning files with the detailed tournament results to find games with each strategy. Later I’ll read the code and decipher the strategy modules more fully. All strategies involve initial buildings placed at the base entrance. On the map Aztec, where the main base is on low ground below a ramp, it smartly builds at the entrance to the natural instead of the entrance to the main. Few bots know to do that; even Locutus got in some trouble by not doing that.

Every one of these strategies is defensive, as you might expect with buildings at the entrance. I think AITP has good vulture micro and would benefit from having the option of more aggressive vulture play. Also none of the openings is honed to a fine edge; they are all a little rough.

A1-B1-B2-C2 Make a very fast barracks at 5 supply, placed at the base entrance, then finish the wall with a bunker and supply depot. This is the build labeled AntiRush. Later add more barracks and get medics (though no marine upgrades) and move out. I initially guessed that AITP played factory unit mixes every game, but this is a barracks unit mix. This plan scored wins only against random UAlbertaBot, which of course favors rushes. Here is a win on Heartbreak Ridge versus protoss zealots.

A1-B3-C2 Make a very fast barracks at 5 supply, placed at the base entrance, then finish the wall with a bunker and supply depot (this part must be A1). Follow up with factory and ebay and turret up the base. Get vultures with speed first, then expand. Eventually switch into a tank-goliath unit mix (I’m guessing this is C2, which if true means that I didn’t see a game with the anti-rush A1-B1-B2-C2 strategy which got as far as stage C). This was AITP’s most successful strategy by far, in fact I would call it the only successful strategy. It was a top choice against ZZZKBot, Microwave, McRave, UAlbertaBot, and BunkerBoxeR. Here is one of AITP’s 7 wins against Microwave on Python where Microwave played a 9 pool and followed up unambitiously.

A3-B5-C1 Narrow the base entrance with supply depot then barracks, and start a factory before the first marine. Get vultures with mines first and remain defensive. Start a command center in the base, and keep making vultures. When the command center is done, float it into the natural, keep defending with the vultures, and finally add tanks before moving out. This strategy scored zero against every opponent except BunkerBoxeR, which played broken builds. In the first game versus Iron on Benzene, AITP put up a creditable fight.

A3-B7-C1 Narrow the base entrance with supply depot then barracks, and start a factory before the first marine. Get vultures with mines first and remain defensive (up to here must be A3). Then get tanks and start a command center in the base. Not a suitable opening against aggressive play. When there are 2 tanks (no siege mode), lift the finished command center toward the natural. This plan did not show good success against any opponent, but worked better than A3-B5-C1 above. Here is a game versus McRave on Destination.

A4-B2-C1 Wall the base entrance with barracks, depot, bunker, in that order. Make marines and vultures and a fast ebay for turrets. When there are 5 marines, push out, bunker the natural, and expand. The strategy does not impress me; it is not greedy enough to gain an advantage against a cautious opponent (DaQin), and as implemented not safe enough to survive an aggressive opponent (Microwave). Watch how effortlessly PurpleWave wins with 2 dragoons and straightforward play when the 5 marines move out; AITP is missing the defensive skills to make it work.

For those like me who have not been paying attention, the CoG 2019 results page has had its broken downloads fixed. The SOURCE_CODE link now gives you source, and REPLAY_04 is a valid zip file full of replays.

Here’s data from Microwave’s history files, using the same script as for BananaBrain with a little customization. Unlike Microwave’s learning files, which deliberately omit data and include information from pre-learning, the history files tell what Microwave actually did during the games. Microwave didn’t record information about the opponent’s strategy, so that table is left out. That made it look a little sparse, so I added columns giving the first and last games when the opening was tried, where the first game in the history file is game 0. We can see things like when a winning opening was found, and whether it kept winning. If there are fewer than 100 games recorded for an opponent because Microwave crashed, then the game numbers generally do not align with the tournament round numbers.

Against difficult opponents, Microwave experimented widely. Against some opponents that Microwave pre-trained against, it played whatever came out of pre-training. So I don’t have much to say about opponents in the top half of the post. But toward the bottom I’ve made some comments. Especially see the note to AITP.

Why are only 71 games recorded? According to the official results, Microwave crashed in 56 games throughout the tournament, and 29 of those crashes happened against ZZZKBot. Microwave recorded every game in which it did not crash. It’s a debugging opportunity. :-/

As soon as Microwave found that 7 pool worked, it played 7 pool exclusively.

Microwave was late to discover the success of the hydra bust opening. That’s why it was played so little. The example shows the importance of finding good ideas as early as possible. I am adding smarts to Steamhammer to make it better at finding the good tries fast.

It’s interesting that 2HatchMuta and 4PoolSoft have the same numbers, but were given up on at different times.

The choice against UAlbertaBot was determined by pre-training. From scratch, I expect Microwave would have tried a wider variety.

Apparently the initial choice against an unknown is random.

I wrote a script to analyze BananaBrain’s game history files, which record its experience with each opponent. For now, I had the script summarize the strategies played and the enemy strategies recognized. The history files also record the map and a value that represents the game duration. History files are rich with information, and there are many ways to summarize it. It would be interesting to see how strategy usage and win rate vary by map, among other possibilities.

The same script should work with minor changes to summarize Microwave’s history files.

BananaBrain had prepared history files for the opponents #1 Locutus, #2 PurpleWave, #5 Steamhammer, #6 ZZZKBot, #7 Microwave, and #8 Iron. Data from the prepared history files was not copied into the write directory. That is different from how Steamhammer and Locutus keep their game records, and it has the nice effect that the tables show exactly what happened in the tournament, from BananaBrain’s point of view.

For each opponent, the left table is BananaBrain’s choice. The right table is BananaBrain’s idea of what the opponent did. All the win rates are from BananaBrain’s point of view, so that, for example, when Locutus played P_1gatecore, BananaBrain won 5% of the time. Of course, the opponent’s view of its own strategy is likely to be more fine-grained than BananaBrain’s. To take the extreme case, Steamhammer played 30 different openings against BananaBrain, and BananaBrain recognized them in 8 categories.

As you might expect against Locutus, the best choice was a fast expansion.

Is the single game of enemy P_fastexpand a misrecognition? I suspect that Locutus played otherwise, and BananaBrain didn’t see everything and wasn’t able to draw the right conclusion. Or maybe it’s a bug somewhere. PurpleWave and McRave also show a single P_fastexpand game.

Against PurpleWave, different zealot rushes worked best. Maybe it is because zealot rushes depend for their success more on execution than on the enemy’s strategic reaction. PurpleWave is particularly good at reacting to the enemy strategy, and BananaBrain is good at execution.

BananaBrain made quite a variety of tries, and was most successful with... zealot rush and dark templars, which are kind of different. BananaBrain’s varied opening choice is a strength.

2 gate zealot openings work well against Steamhammer—but only when played by PurpleWave or BananaBrain. Steamhammer can usually defend versus a lesser protoss.

I like that BananaBrain varies its opening choice even when several openings win 100%. (Steamhammer does too; if more than one opening has scored 100% so far, Steamhammer chooses randomly among them.) Playing a strong opening gives the opponent one problem to solve (“how do I survive this?”). Unpredictably playing one of several strong openings sets the opponent two problems (“what is this fiend doing, and then how do I live through it?”) which must both be solved, more than twice as difficult.

Bulldog! That involves protoss dropping zealots, typically on cliff tanks, with a simultaneous attack by ground. When successful, a bulldog can abruptly break a terran defense that is sound against any purely ground attack. I don’t think I’ve seen BananaBrain play that; I should watch more games versus terran. Can anybody point out an example?

The Stove worked against XiaoYi? Again, XiaoYi shows weakness against tricks. The Stove involves making scouts to harass while teching to dark templar. It should not be hard for a good terran to defend against; notice that Iron dealt with it well enough.

It looks like most openings performed similarly against McRave, and BananaBrain struggled to identify what worked. I imagine a fierce learning battle, both trying to keep one step ahead.

BananaBrain apparently does not have a bunker rush recognizer.

AITP scored zero against over half of the participants, so its learning results are not deeply interesting. Also, its strategies are labeled with opaque sequences of letters and numbers. But it was easy to generate the tables, and they offer a little insight into AITP’s interesting design, so here they are.

Unlike other Steamhammer forks, AITP does not spell out concrete opening builds in the configuration file, at least not beyond 4 x SCV—start by making workers. The strategy names themselves are code sequences that tell what to do throughout the game. The letters A, B, C are stages of the game, and the combinations A1, A2 etc. are “modules” that may be active during the matching stage. Each module has its own update method to decide what to build, and the StrategyManager sometimes checks the current module for other decisions. There is module switching code in case of surprises (StrategyManager::shouldSwitchModule()); it also sets flags and updates other information.

I like it, it’s a flexible way to specify a plan for the whole game, and allows for changing plans on the fly. It’s an abstract strategy system, similar in principle to what I plan for Steamhammer. My implementation will look entirely different, though.

AITP has only 5 strategies configured. I gather that it can switch to other sequences on the fly if circumstances warrant. 5 is not many, though; I think they have only completed the basics. Here is the Steamhammer opening group it assigns to each strategy. It does not use the opening group strings, but they may have some heuristic value:

• A1-B3-C2 AntiRush
• A1-B1-B2-C2 Rush
• A3-B5-C1 NoneBunker
• A3-B7-C1 NoneBunker
• A4-B2-C1 8BB (does that mean BBS?)

AITP lost every game, but did not explore its possible strategies equally. It seems to have priorities. Maybe later I will look into how that works. AutoGasSteal is set true in the configuration file, but AITP did not record itself as stealing gas against any opponent. Presumably it is turned off in the code.

Steamhammer is the highest-ranked opponent that AITP scored wins against. It looks like a few scattered games, though.

It looks like ZZZKBot played its 4 pool in over half the games, and perhaps its guardian rush in the remainder. A1-B3-C2 is the strategy labeled AntiRush. AITP recorded more wins for itself than it actually scored, despite recording fewer games than it played. I suspect that AITP has changed the meaning of the numbers.

Again, AITP recorded fewer games and more wins than happened. Is it a bug, or is it intentionally over-recording wins for certain strategies to focus its search? Or what? AITP is interesting, it deserves a closer look into the code.

DaQin is derived from Locutus and also keeps 200 game records. But DaQin did not have pre-learned data. No games were left uncompleted; there are 100 against each opponent.

DaQin plays fewer builds than the other bots I’ve looked at so far.

DaQin had an enemy-specific strategy configured for Locutus, so it didn’t try anything else. Locutus is the only opponent that DaQin tried to prepare for, as far as I can see.

DaQin incorrectly recognized dark templar rush as Locutus’s strategy in most games, then correctly recorded that no cloaked units were seen during the game. See yesterday for Locutus’s play against DaQin, which did not include any DT build. I assume that the dark templar recognition is deliberately over-cautious, because DTs are dangerous. Locutus does have a fake dark templar build, where it adds a citadel of Adun to fool opponents into expecting dark templar (it works against most UAlbertaBot-derived bots).

Against PurpleWave, in contrast, DaQin less often foresaw dark templar, but apparently often faced them. (Arbiters can’t get out that fast.)

How did ZZZKBot upset DaQin? These numbers suggest zergling bust (it could be hydras, but DaQin does have a hydra bust recognizer which did not fire): Mostly “heavy rush,” few mutalisks, no lurkers. Steamhammer also settled on zergling bust as the best bet, but was much less successful. Microwave tried its zergling bust build versus DaQin without success. Maybe ZZZKBot’s extreme aggression is the key.

12NexusCarriers seems to be the default build versus terran. Apparently terrans, even Iron, were not able to punish the fast expand. Well, they’re not supposed to be able to without risk, that’s the point of cutting probes for nexus on 12, but it does require good play from protoss to ensure.

XiaoYi usually got air tech pretty fast, that’s unusual and interesting. I’m guessing it scouted the carriers coming and prepared wraiths.

McRave upset DaQin. Dark templar in 3 out of 4 games, and they came out pretty early. PurpleWave showed a similar pattern, but it wasn’t as salient because it wasn’t an upset. The dark templar rush recognizer did not seem to be fully effective, possibly because it was overridden by the fast rush recognizer. DaQin’s best counter was DT-back-atcha.

DaQin had some trouble adapting to random UAlbertaBot. This is a point where preparation for the opponent would have been valuable: Make a build that UAlbertaBot can’t beat and ensure that it is played. It can be a general-purpose build; PurpleWave included a cannon turtle build that is safe against all sorts of rushes.

Beating BunkerBoxeR with a build of fast expansion into carriers is... not the intuitive choice. But I guess it worked.

New bot Feint appears to be a feint. It looks like a recompiled Steamhammer 2.3.5 playing terran; the DLL files are the same size in bytes, and even the bot and author names in the configuration file have not been touched. It this a test and it will be updated, or is it a throwaway?

Locutus’s game records are in almost the same format as Steamhammer’s and can be summarized by the same script. I expect it will also work for DaQin and AITP.

Where Steamhammer was set to keep 100 game records per opponent, Locutus was set to keep 200. Since there were 100 rounds in the tournament, game counts over 100 mean that pre-learned data is included in the table alongside the tournament data. If Locutus was not trained on a near-final version of the opponent, then the two could be significantly different.

After seeing a few Locutus-PurpleWave games I got the impression that PurpleWave reacted adequately to Locutus’s trick strategy of cannoning the ramp and then dropping zealots. So I was surprised that Locutus considered it the best choice. But the overall win rate is high compared to the tournament results, so I suspect it is influenced by pre-learned data from games against a weaker version of PurpleWave.

The numbers in the “recognized” columns of the plan table show how widely Steamhammer cast its net for a solution to Locutus.

Locutus never tried to steal the gas of a zerg. Objectively, that makes sense. In the context of bot play, I’m not so sure; many bots of all races mess up their builds in the face of a gas steal.

Locutus declared an enemy-specific strategy against Iron. I’m not sure why it also had pre-learned data.

Proxy DT rush. That tends to confirm my picture of XiaoYi as vulnerable to tricks.

Locutus configured an enemy-specific strategy against UAlbertaBot. Openings other than CannonFirst4GateGoon are from pre-learned data, which was ignored in making the opening decision.

Today is Steamhammer. With a mid-rank finish and the widest range of builds, plus informative game records, Steamhammer may give us the best insight into how other bots played.

The tournament was 100 rounds, and Steamhammer was configured to remember the previous 100 game records, because in play there is no reason to remember more (earlier records are increasingly discounted). Steamhammer also had pre-learned game records for many opponents, so when the game record count reached 100, new records added caused old pre-learned records to drop away. Not all 100 tournament games happened for each opponent, but the pre-learned games filled in the small gaps so that Steamhammer ended up with exactly 100 game records per opponent in every case.

The “opening” table counts Steamhammer’s opening choices. The “plan” table shows the plan that Steamhammer first predicted that the opponent would play, then recognized that the opponent was playing. Both prediction and recognition can be wrong. The timing table is new this year, an attempt to get a little more information out of Steamhammer’s rich game records. For some events, it gives the count of games in which the event occurred, and the median time, earliest time, and latest time it occurred in those games when it did. The times are given under the assumption that 1 second of game time is exactly 24 frames, a simplification.

• gas steal attempt - When Steamhammer sent out the drone to steal gas (if it did).
• gas steal success - Whether the gas steal attempt succeeded in taking the opponent’s gas. Steamhammer doesn’t record the time it happens, so this is only a success count.
• enemy scout - When the enemy scout first reached Streamhammer’s base.
• enemy combat units - When the first enemy combat unit was seen.
• enemy air units - When the enemy is first known to have tech for flying units (except overlords).
• enemy cloaked units - When the enemy is first known to have tech for cloaked units.

It looks like Locutus opened forge-expand every game. It worked. Steamhammer desperately tried everything, including ZvZ builds and island builds, and finally squeezed 3 wins with a risky extreme macro opening, 4 hatcheries before spawning pool, which was able to win one game in five. I should add 5 and 6 hatch before pool and see if they help.

Locutus rarely made corsairs or dark templar. I wonder what its criteria are? Maybe it won before it got that far. The scout was usually quite early, and the first combat unit was seen late, as expected for a cannon-first opener.

I played over the 3 wins. They were in rounds 65, 70, and 73; after that, I expect that Locutus found a way to win. In 2 games, Steamhammer pulled ahead in early economy with its greedy opening, then struggled to defend and fell into a losing position. But Locutus got most of its units stuck in its base, and Steamhammer was able to turn it around and win after a hard fight with critical defiler support. In the third win, Locutus chose a zealot-archon unit mix that Steamhammer knows how to cope with, and zerg powered through.

PurpleWave in contrast went with mostly 2 gate openings against Steamhammer; that’s what “heavy rush” means for protoss. Steamhammer countered with early sunkens plus hydras or, less successfully, mutalisks (this version had a bug that weakened mutalisk play). There are also 3 wins with a ZvZ fast mutalisk opening. 2 gates should beat that, so protoss either played poorly or chose a different build in those games.

BananaBrain contrasts with both previous opponents in that it played a variety of builds. Steamhammer was unable to predict what was coming. It looks strange that the best reaction was an opening designed to counter terran factory-first builds that include a vulture runby, but in fact it is a mildly specialized 2 hatch mutalisk variant and not so surprising. BananaBrain made corsairs and dark templar in most games.

DaQin played forge-expand and has similar timings to Locutus, for the same reasons. The fast scout is to allow adjustment of the cannon count and timing, and the late combat units are due to getting a gateway later. Steamhammer couldn’t find any better reaction than to try to bust with zerglings, either early or late, and it was not particularly successful.

ZZZKBot mostly played a turtle-into-mutalisks strategy against Steamhammer, and was somewhat successful. You can read the idea straight out of the tables above. The 2:25 earliest timing but 4:06 median timing for combat units says that ZZZKBot sometimes rushed zerglings, but usually not.

Microwave played a 9 pool speed build into expansion and then spire, which you cannot read out of the plan table because Steamhammer didn’t recognize it accurately. But in the timing table you can see that combat units (zerglings) were early and air units (mutalisks) were not late.

Steamhammer was not able to steal Microwave’s gas. It probably should have stopped trying.

Look at that huge range of scout timings! 0:34 means that the scout SCV was sent immediately at the start of the game and went directly to the zerg base. 15:09 probably means that no enemy unit got into the base until the end of the game when Steamhammer lost (Steamhammer is on BWAPI 4.1.2 and cannot detect scans). Steamhammer prevented the scout entirely in 13 out of the 100 games by its own count; 15:09 is probably the same. Steamhammer was not able to steal Iron’s gas, and did eventually give up trying.

Steamhammer liked 7 pool against XiaoYi, just as Microwave did, but also liked its dawn hydra rush.

Thanks to pre-learning, I expected Steamhammer to play its overpool turtle build every game. I’m not sure why it didn’t. I also don’t know how it hit on the 5 scout build, which means send out a drone at 5 supply to scout very early, then leave all decisions to the strategy boss. It’s a logical try against a random opponent, especially one that has a single strategy for each race, and it was fairly successful. But it did not appear in the pre-learned data.

AITP scored zip against both mass zerglings (9HatchExpo9Pool9Gas) and against fast mutalisks (AntiFact_13Pool). And it successfully scouted Steamhammer’s base only 25% of the time. If you don’t scout reliably, it will be hard to withstand rushes.

Steamhammer was not able to judge whether BunkerBoxeR was playing a proxy (with its proxy bunker) or a worker rush (since it sent SCVs in support). But it didn’t matter. The reactions are nearly the same. Since BunkerBoxeR never wants gas, stealing its gas was a waste.

Steamhammer knows 142 different openings. In the whole tournament, it was only able to try 111 of them! It tried the most openings versus protoss, since it was looking everywhere for an escape from the overwhelming top protoss bots. Most openings were tried only a few times and lost every game, which means that Steamhammer would have performed better without them. That’s expected and even intentional; my plan is to add smarts until it is able to make good guesses about what to try. The work is underway.