Zealots/muta/dragoons/Hydralisks is just a standard rock/paper/scissors game theory thing, and it shouldn't be too hard to calculate an approximate nash equlibrium. The problem is that there is micro, macro, game theory, imperfect information, and an AI has to tie all these different aspects together (as well as perhaps some perceptual chunking to reduce the complexity) so its a real challange for combining different cognitive modules. This is too close to AGI for comfort IMO.
This is too close to AGI for comfort IMO.
Pretty sure it's still comfortably narrow AI. People used to think that chess required AGI-levels of intelligence, too.
So chess and Go are both games of perfect information. How important is it for the next game that DeepMind is trained on to be a game of perfect information?
How would the AI perform on generalized versions of both chess and Go? What about games like poker and Magic the Gathering?
How realistic do you think it's possible to train DeepMind on games of perfect information (full-map-reveal) against top-ranked players on games like Starcraft, AOE2, Civ, Sins of a Solar Empire, Command and Conquer, and Total War, for example? (in all possible map settings, including ones people don't frequently play at - e.g. start at "high resource" levels). How important is it for the AI to have a diverse set/library of user-created replays to test itself against, for example?
I'm also thinking... Shitty AI has always held back both RTS and TBS games.. Is it possible that we're only a few years away from non-shitty AI in all RTS and TBS games? Or is the AI in many of these games too hard-coded in to actually matter? (e.g. I know some people who develop AI for AOE2, and there are issues with AI behavior in the game being hard-coded in - e.g. villagers deleting the building they're building if you simply attack them).