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lackofcheese comments on Superintelligence Reading Group - Section 1: Past Developments and Present Capabilities - Less Wrong
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Do you know of a partially observable game for which AI lags behind humans substantially? These examples are of particular interest to me because they would significantly revise my understanding of what problems are hard and easy.
The most prominent games of this partial information that I know are Bridge and Poker, and AI's can now win at both of these (and which in fact proved to be much easier than the classic deterministic games). Backgammon is random, and also turned out to be relatively easy--in fact the randomness itself is widely considered to have made the game easy for computers! Scrabble is the other example that comes to mind, where the situation is the same.
For Civilization in particular, it seems very likely that AI would be wildly superhuman if it were subject to the same kind of attention as other games, simply because the techniques used in Go and Backgammon, together with a bunch of ad hoc logic for navigating the tech tree, should be able to get so much traction.
I wouldn't say that poker is "much easier than the classic deterministic games", and poker AI still lags significantly behind humans in several regards. Basically, the strongest poker bots at the moment are designed around solving for Nash equilibrium strategies (of an abstracted version of the game) in advance, but this fails in a couple of ways:
1. These approaches haven't really been extended past 2- or 3-player games.
2. Playing a NE strategy makes sense if your opponent is doing the same, but your opponent almost always won't be. Thus, in order to play better, poker bots should be able to exploit weak opponents.
Both of these are rather nontrivial problems.
Kriegspiel, a partially observable version of chess, is another example where the best humans are still better than the best AIs, although I'll grant that the gap isn't a particularly big one, and likely mostly has to do with it not being a significant research focus.