Luke Dicken wrote a very nice blog post the other day on the differences between how AI is viewed in academia (and among students) and in the game industry. He focuses on the real-time requirements of game AI, and how little processing power most games make available for AI. He also talks about how NPC behaviour in games needs to be entertaining, not just high-performing, and reminds the reader that in striking contrast to e.g. AI for robots, game AI is allowed to cheat if it makes the game better.
Kevin dill wrote a response post where he points out that "traditional AI" is simply trying to solve very different problems than those faced by AI in games. Far from being simplistic and primitive, the techniques devised specifically within game AI are well suited to their specific purpose: reliably providing interesting NPC behaviour, while being understandable and moddable by designers. Perhaps academic AI research should take a hint or two from industrial game AI, rather than the opposite.
I am not fundamentally disagreeing with anything Luke or Kevin say. I think they both make several good points. However, I'd like to point out that I have a rather different and, I think, broader perspective on what game AI is. Both posts make implicit assumptions on what games are and what AI is, which I think are limiting.
The main game examples used by Luke and Kevin are Red Dead Redemption, Dragon Age, Battlefield Bad Company 2 and Left 4 Dead. While these are impressive games, they are all representatives of a pretty small subspace of gaming: AAA first-person story-driven games with a real-time component and graphics that require them to run on a home console or computer. They are also all targeted at a classic "hardcore gamer" audience. These are the sort of games that we typically talk about when discussing games, and these are the sort of games that everybody wants to work on. But not the games that most people play. It's like if in the automotive industry, everybody would want to work on the next Porsche, while most people drive a Toyota.
Bejeweled, FarmVille and Diner Dash don't have a first-person perspective, don't have complex graphics and don't have a story in the same sense as the games above. Yet as far as I know, they have more players than those games. Importantly, they don't have NPCs that need to be controlled by AI, but still they present a number of interesting AI problems. Even traditional hardcore strategy games like Civilization, StarCraft or Total War present hard AI problems which are only insufficiently solved by the techniques used in the game industry.
The other limiting assumption is that AI is used for controlling NPC behaviour. In fact, this is only one of many applications for the bountiful toolbox of techniques found in artificial intelligence. AI techniques can also be used to generate game content (levels, maps, rules, puzzles etc), model players, adapt various aspects of the game (such as the difficulty or the reward schedule), match players in online games, control artificial economy, debug game mechanics or implementations, and so on.
Between all the myriad types of games out there and the multitude of interesting AI problems within them, I feel there's more than enough to work on even for an academic like me. Real-time pathfinding and planning for FPS and RTS games is all great, and I look forward to playing the results, but I'm happy to see someone else doing that specific work.
If you're interested in the "other" game AI work I'm involved, you might want to read our recent survey papers on generating game content and on adapting games based on player models.