@AlbertoT: The problem with the algorithms you mentioned is that you need lots of training data to get a decent AI. In some cases, like reinforcement learning, it is possible to boost learning by training phases in which the computer plays against himself (followed and preceeded by seesion in which the computer plays against a human being), but this requires more or less that the game is already more or less balanced.

From what I have seen I believe that this is perfectly possible for board games like backgammon and chess; and (team-)sports games, like soccer, icehockey, baseball and tennis.

Though, games tend to be a bit "arcade" like (or in other words: accessible to casual gamers), so virtual characters run faster and jump higher and such. Personal skills play nowadays also a big role in sports games (like in Fifa) and they blast the variables into infinity; I believe that for neural algorithms it is already too hard to capture general strategies and I think it is impossible to capture personalized skills as well (when playing against Real Madrid you try for example to take out Xabi Alonso out of play and treat Christiano Ronald in a different way, compared to your regular soccer team opponent).

Because of these overwhelming set of variables and requirements of the player to a game, tweaking a handcrafted state machine seems to be more convenient to game designers, for which they can try out different things like "lets see how hard the AI is if it can produce Tank Units 50% faster than before" or "lets give the Beast half health points because it is already too hard for the casual gamer". It is for example unclear how Reinforcement Learning encodes the "knowledge" and "experience" in practice and this is a no-go for developing commercial games.