I posted a question to Ask Slashdot a few days ago, which recently got up on the front page and has at the time being had 190 replies:
What Would You Like to See from Game AI?
(Incidentally, it seems to have generated quite a bit of traffic to this blog as well...)
So what can I say about the answers? Well, there seems to be a lot of variance in what people want, or think they want. The two most common requests are AI that learns (from the player, or from past behaviour) or just "lives its own life", and AI whose reasoning is understandable/transparent/believable. While I certainly agree with the learning and adaptation bit - that is very much what I am working on - I am not so sure about the other request. Architectures that learn are generally not very good at explaining why they do what they do, but usually very good at coming up with behaviour you didn't expect when you designed them.
Do people really want to understand why agents in games do what they do? And to what extent? Surely it becomes boring when you can predict every move of your opponent, but I suppose a game where the opponents act seemingly randomly is not so fun either. Maybe there's a tradeoff. But then again, isn't there a difference between seemingly random behaviour, and seemingly random but obviously effective and therefore somehow goal-directed behaviour? The sort of difference that makes people go to great lengths to understand the behaviour?
Of particular note is the poster who complained about racing games that adapt their difficulty level to the human player, and thus ends up winning in the last lap however well you drive. But that's not AI. That's just adapting a difficulty setting. A game that not just adapted to your skill, but instead adapted to counter your particular driving style, would be much more interesting. And you would learn more from it.