Starcraft AI blog

Here is what the protoss AIUR learned about each opponent over the course of AIIDE 2018. . Seeing AIUR’s counters for each opponent tells us something about how the opponent played. For the recent CIG edition, see CIG 2018 - what AIUR learned.

This is generated from data in AIUR’s final write directory. There were 103 rounds played (100 of which were official) and 10 maps, three 2-player, two 3-player, and five 4-player maps. For some opponents, all games were recorded; for the supernumerary 3 rounds at the end, the extra games were on the 2-player maps (they’re taken in rotation). For many opponents, fewer than 103 games were recorded. AIUR recorded 2606 games in 103 rounds, and officially played 2570 in 100 rounds. 2570 plus the 3 extra rounds times 26 opponents per round gives a total of 2648, which is 42 more than AIUR recorded. There were 37 official crashes in 100 rounds, leaving 5 games unaccounted for. They might be crashes in the extra 3 rounds. It’s also possible that the last round was not finished.

It would be nice if we had the data after round 100, instead of round 103. We could do the accounting and get correct answers.

First, the totals across all opponents.

• 2, 3, 4 - map size, the number of starting positions
• n - games recorded
• wins - winning percentage over those games
• cheese - cannon rush
• rush - dark templar rush
• aggressive - fast 4 zealot drop
• fast expo - nexus first
• macro - aim for a strong middle game army
• defensive - try to be safe against rushes

AIUR struggled in this tournament; it has not been updated since 2014. As in CIG, AIUR did about equally well on the different map sizes, but relied on a different mix of strategies on each. On all map sizes, the defensive strategy was most often used. On 2-player maps, the cannon rush was also a popular solution, and on 4-player maps (where cannon rush is harder to pull off), the dark templar rush and the nexus first fast expansion were popular.

As in CIG, AIUR’s learning is able to squeeze a little extra from the toughest opponents. Against #1 SAIDA, it found that the dark templar rush occasionally worked, and was able to get a couple extra wins on 4-player maps. The same plan scored a single win on a 2-player map, but repeating the strategy did not help. Nothing else it tried made a dent.

Oops, I lied already. AIUR was not able to squeeze an extra win against CherryPi. It won a total of 2 times with different strategies, and repeating the strategies did not win again. This is the first time I have seen AIUR’s diverse strategies unable to make any impression.

CSE was apparently not fully prepared for cannon rushes. AIUR plays the best cannon rush of all bots, in my opinion. But even the best is harder to pull off on a 4-player map.

The Locutusoids showed somewhat similar patterns. BlueBlueSky was surprisingly weak against the cannon rush.

The other part of the pattern is some weakness against dark templar rush. Interestingly, the earlier version of Locutus survived AIUR’s DTs perfectly in CIG, despite a fair number of tries.

McRave shows a different pattern. Its weaknesses were against the cannon rush on smaller maps and nexus first on 4-player maps—a fast rush versus a macro opening. The tournament manager cycles through the maps in order, which makes a difference for bots which are sensitive to which map is being played. It’s possible that the sequence of strategies that AIUR played as the maps cycled through helped confuse McRave’s learning.

AIUR of course settled on the defensive opening against ZZZKBot, which prefers 4 pool.

The fast expo (“big army later”) and the defensive opening (“some army fast”) play out similarly when Steamhammer does not go with an early pressure opening. Maybe that’s why they both found some success.

That is quite a variety of tries against Microwave!

LastOrder may have been trained offline against AIUR (that would fit with how LastOrder is supposed to work).

MetaBot includes AIUR as one of its heads. Also AIUR struggles against both the other heads, Skynet and XIMP. Still, aggressive tries had some success.

UAlbertaBot is one of the opponents that AIUR has pre-learned data against. The pre-learned data is not included in this table. That’s why so many cells are 0.

XIMP is the other competitor that AIUR has pre-learned data about.

It smells like CDBot played a rush every game, and not a strong one.

Looking across all the tables, each of AIUR’s 6 strategies was sometimes found to be the best. Even today, the variety remains valuable.