When writing my earlier blog entry on how you can get complex behaviors from very little and why it’s okay to fake AI (and anything else), I came across this slightly insulting comment about game AI vs. academic AI research that I just have to share (emphasis added):
As someone working in new (bio-inspired) AI research with an eye to applications in games, but within an academic setting, I often hear that game developers are not incorporating cutting-edge academic AI into their projects because it’s too “risky” (they can’t really predict how gamers would react), and because they don’t see the point in it. As a gamer, and as someone who cares what gamers think, I am often surprised by the sorry state of current commercial game AI – it has hardly moved since the 1980s. However, maybe the problem is that no-one really knows what we want from game AI. Academics keep coming up with innovative AI technologies, but what we should we use it for? What do you think? What sort of intelligent behavior would you like to see in games, but don’t at present? Which are the most obvious intelligence deficiencies of current NPCs that need to be fixed?
Author of this quote is Julian Togelius, an academic exploring the use of “computational intelligence” for games. (Computational intelligence, for those who aren’t hip with the latest lingo, is sort of an newfangled umbrella term for neural networks, genetic algorithms, and all those other generic biologically-inspired computational approaches you might grab for when you don’t have a clue about what you’re doing, but not otherwise.)
Togelius’ work seems to be focused on evolving racing game AI in particular, and some of his highlights can be found attached to this blog entry. Now, I’m particularly fond of this sample of “evolved” racing car AI:
Awesomely innovative and cutting-edge huh?
This movie is accompanied by another rather priceless quote (again my emphasis):
The last example shows that we still have some way to go: sometimes the outcome of the race is that both cars find themselves stuck against some wall. So far, the cars haven’t found out how to back away from a wall and resume the correct course. We’re working on more complex neural networks and sensors to overcome this.
Of course, I hope that these technologies will some day be incorporated into actual racing games (which shouldn’t be impossible, looking at the sorry state of most game AI) and physical vehicles. But along the way, I think there is a lot to be learned about how the evolution and learning of behaviour works, and I think that games are the ideal environment for doing that research in. More on that in a later post.
Um, yeah, no. Don’t let the door hit you on the way out.
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