Starcraft AI blog

One of my goals for Steamhammer is to remove the dependency on BWTA, a large, slow and troublesome library. Its startup time to analyze a new map for the first time is ridiculous, and I want full power to analyze map blocks. I could go to BWEM, and I may yet change my mind and do that. But I’m not 100% satisfied; I might have to modify the library to simplify hypothetical reasoning and add features like pathfinding for units which are pushed through minerals. Another idea is to start with UAlbertaBot’s existing distance maps. It calculates ground distances at build tile resolution (32 x 32 pixel tiles) rather than the walk tile resolution of the map (8 x 8 pixel tiles), but this doesn’t cause any obvious problems and is easy to change if necessary. UAlbertaBot doesn’t actually rely on many BWTA features.

I was leaning toward the native distance map solution already. It calls for writing more code, but not that much, and the final solution would end up simpler and better tailored. Now Dave Churchill has made exactly that change to UAlbertaBot. It smooths my path since I can borrow code, and I plan to follow.

Dave Churchill removed BWTA big-bang style from UAlbertaBot, dropping the dependency and replacing its major uses in one step. I think the largest piece was to add a BaseLocation class—not a large piece at all. My implementation strategy will be the opposite. I’ll replace uses of BWTA item by item, testing as I go, and when I’m satisfied I’ll drop BWTA. The development process should be gradual and stable.

As a first step I taught Steamhammer to pay attention to map blocks in calculating ground distance maps. It doesn’t notice when blocks are removed, or route units around blocks, it only calculates distances more accurately based on the map’s initial state. Even this small step improves play, since Steamhammer decides on expansion locations partly based on ground distance. Since I reflexively clean up any code that I touch, I also fixed a bug in checking whether a tile is walkable and reduced the unnecessarily large size of the data structures. Distance maps are better, faster, and cheaper—there should be less memory traffic in calculating a map, and lookups will be more compact in cache.