tournaments and tournament preparation
The CoG conference is underway from today through Thursday. It is of course entirely online in this plague year (did somebody let a defiler loose?). I expect the CoG 2020 tournament results to appear during the conference or not long after. The conference program does not announce a time, as far as I can see. You can look back at my expectations if you like.
I am working hard to prepare Steamhammer for AIIDE 2020. I have already uploaded 3 different test versions one after another to the Starcraft AI Ladder, and I have made good progress on the next test version to go up in a day or two. Each version shows some kind of progress in its play, though the win rate does not always go up. The AIIDE update will be much bigger than the update to the current 3.1 release.
I have been watching a huge number of replays and analyzing results with my software. The purple Dan Gant talked in an Undermind podcast about his tournament preparation process: He examines losing games only, identifies the most frequent game-losing weaknesses, and works to fix those. It’s great method, a direct way to improve tournament results in the short term, especially for a bot which can expect to finish near the top, meaning that it shows relatively rare game-losing weaknesses at its level of play. My goal is different, and I follow a different process. I also want to finish well, and make low-risk short-term improvements, but my top goal is to improve play in the long run rather than the short run. I identify ahead of time aspects of play that I think I can and must improve, and I examine both winning and losing games with an eye on those aspects, often looking at specific opponents that bring out those aspects. The weaknesses in winning games must be fixed too; they are by definition not game-losing, but fixing weaknesses improves play and that’s that, and to my eye winning games often show glaring blunders by Steamhammer. Anyway, at Steamhammer’s level of play, fixing a game-losing weakness often leads it to go wrong in a different way a little later and lose anyhow, so I may have to fix a string of weaknesses before I see results. Those are compound weaknesses, as it were, and I run into a lot of them.
I have learned some things by analyzing the ladder results with my software. One of the things I learned is that I could figure out even more if I wrote more software, and if I had Steamhammer record more information that I could correlate with the game results. I may do that soon, and post data.
The Ladder has new participants since I last wrote about it. Besides Stardust, MadMixP is new and StyxZ is reactivated. Just today, CherryPi joined too. That makes 11 active participants, enough for a small but lively scene. I still recommend joining the ladder if you intend to compete in AIIDE 2020, because it is the best way to test that your bot runs correctly in the AIIDE environment, adhering to the strict time limits and so on. Just letting it run for a day is enough to test for basic correctness (the ladder runs games much faster than BASIL), and if you have a problem you want to find out about it early so there’s time to fix it.
Comments
Dan on :
The CherryPi on there is the AIIDE 2018 version which I uploaded last night on a whim to see if that specific distribution would run on the SAIL environment, an experiment which appears to be failing as I'd expected it would. That distribution links against an existing CUDA installation, which itself depends on having a GPU which VM setups like SAIL generally can't provide (The annual tournament's GPU games run on bare metal). Would be nice to get it playing games on ladders (it'd still be a top 8-10 bot today) but it needs some surgery to excise the CUDA dependency.
Jay Scott on :
Nothing prevents a short-term gain from also being a long-term gain. It is like adding a brick to a wall. If the wall is sound, the addition is permanent.
Measure-and-improve is the only reliable form of improvement. The other possibility is to get lucky, which is not reliable. The way I look at it, if the measurement is solely of win rate, then the improvements are 100% short term, I would say by definition. If the wall is sound, they might be 100% long term too. Probably only algorithms make improvements solely by win rate measurement; a human author will use judgment. What I meant when I said that I seek long-term improvements is that even when spending effort to prepare for this tournament, I divert some effort to winning next year when it has no effect on this year; you could say I am intentionally wasting effort in the short-term for a later gain. For me it is a deliberate goal; I am never only preparing for the next tournament.
@Mi1996 on :
Jay Scott on :