• Robotics: Irrelevant to games, and for that matter not terribly relevant to me and the research I'm doing. Robotics pretty much covers any way that the machine can influence its own environment. For the research I'm doing, we have dedicated mechanical and electrical engineers for that. The closes I get is making simulations, and the other people always just feed me the specifications for those.
• Computer Vision: Also not terribly useful, computer vision is exactly what it sounds like- making computers capable of gathering visual data. This is a LOT more difficult than it sounds, and a bit above my pay grade.
• Speech recognition/language processing: speech recognition involved turning audible sounds into words. Language processing involved turning those words into meaningful ideas. At the moment, the technology is pretty weak (as Siri users can attest), but once it takes off I think it could have a big effect on games, particularly in expanding the diplomacy system- imagine computer players that you could actually negotiate with, or conversable characters in RPGs.
• Knowledge representation & reasoning: the first one is actually already done for us- knowledge representation basically just means putting the game in a computerized format. Reasoning is... kind of the holy grail of strategic AI- it can think rationally, like a human, and solve problems through logic. There has been some success using reasoning in mathematics, but I very much doubt anyone has put it in a game as it becomes VERY computationally intensive.
• Machine learning: ML has grown a lot recently, and is being used in everything from Facebook to spambots. Using machine learning, a computer can build associations between causes and effects, predicting events in systems that are too complicated to calculate directly. I don't honestly see that much application in games, although it could guess at player strategies or make very broad decisions about the course of the game. The big problem is that machine learning requires massive databases to learn from- that means game histories would have to be uploaded to some central location in order to train the thing in a timely fashion. The BIGGER problem is that machine learning starts out with very high error margins on its predictions- I don't know how many people would play a game where the AI had to be constantly updated, and started out as a very poor player.
• Planning. Planning is mostly searches. I've talked about search trees a bit before here, but the short version is that it's computationally intensive (although not as bad as reasoning), and used by things like chess programs. It's rare, but not unheard of, in game AIs.

Also ran a quick Google search for the "Berkeley Overmind" you mentioned, and the AIIDE Starcraft competition it competes in. Very cool project. It reminds me of the Loebner Prize, but with less grandstanding and more complex AIs. I kind of wish it was better publicized and funded.

Yes, in general, RTS AIs can get an edge with APM, but I'm really just talking about the decision-making at a high level - the part that actually relates to strategy

SC AI is easy to write because the game is extremely simple. The AI I wrote that beat the Overmind was less than 50 hours of work. Now that's nowhere near enough time for a good AI, but it barely scratches the surface of what's possible. It's just sad that professional developers do so little when they have so many resources to spend.

I actually think that those AI competitions are counter-productive, because they are about real-time games which shift the focus to a bunch of things that have nothing to do with artificial intelligence. The vast majority of work that goes into any quickly-made but competitive Starcraft AI will have nothing to do with strategy. I hope we get more stuff like that, but it should not be Starcraft.

4X games are a lot more complicated than Starcraft, but nowhere near as resource intensive. 10 years ago, compute resources were truly limited and we totally excused crappy AI, but that excuse doesn't fly anymore, especially for today's turn-based games.

It's really just a matter of effort and prioritization. It's hard to really understand how backward development is without being a programmer yourself, but for those of us who have grasps on both strategy and programming, it's a truly pathetic situation. It's holding back strategy games so much, and it's tragic.

Part of the problem may be in the different memory systems used by computers and humans. Even though there have been some advances on that front thanks to artificial neural networks.

Pretty much like attempting to solve a math problem by plugging in every possible answer until the right one is found.

Things like this are why I loathe having dyscalculia. I can comprehend generalities, but complex mathematics is beyond me and likely always will be. I will never be able to fully pin down why a particular example of AI (game or otherwise) is "good" or "bad" in a specific sense, because my brain simply can't comprehend the mathematics underlying it. All I can discuss is my own entirely subjective experience of playing against it.

Fascinating subject nonetheless, though.