A subscriber of Think Artificial wrote to ask me about games and AI. In short, DF asked what my thougths are on AI in games and which ones I think are the most intelligent.
To answer this bluntly: Game AI is very different from it’s non-game counterpart, and it’s not my field of study. I’ve only compared modern games through a window. However, Alex of AIGameDev has superb coverage of AI in games and the top AI games of 2007, by community vote. The top of the line are Half-Life-2.ep.2 and BioShock.
But regarding Game AI in general: modern games are horribly void of intelligence. It depends on where you set the bar, certainly. There’s tons of AI in modern games compared to 5 years ago. But the first thing to note is that Game AI is not the same as AI. It’s a subset of it. Just like discrete mathematics are a subset of mathematics. And moreover, Game AI is a very specialized subset—it has well defined goals, models for construction and limitations.
Games are governed by laws of commerce first, then innovation.
A game is governed by different laws than academic, general AI R&D. It’s a commercial product, and commercial products depend on older methods wherever possible—methods that have proven successful. Most (sane) business men do not put all their money on a new and untried idea because they don’t know if it will succeed. So, most of commercial products are bulked up with a lot of things that’ve been successful in the pastand then leave a breadcrumb for innovation.
This is very different from academic AI research where the point is to do things that’ve not been done before.
Now aside from these drastically different goals of commercial ventures and academic ones, a game’s purpose is to entertain. As long as the player is entertained it doesn’t really matter what goes on under the hood. Because of this, there’s a certain witch hunt that takes place in the game industry:
A modern game developer is on a mission to slaughter innocent intelligent processes wherever possible.
A game AI developer tries as hard as he can (usually at the bidding of a project manager) to minimize intelligence. One reason is that intelligent processes are massive processing-power hogs. Thus, like an obese overeater the systems must forcibly give away every other meal to accommodate an average person’s desktop PC — and those machines don’t have much elbow-room to replicate the massive crimson jelly residing in the heads of animals. Human or other.
And then there are graphics, another obese overeater, who also need a place at the table. And because games are governed by the laws of commerce, Game AI must leave at least five chicken wings more than it ate itself for its obese, graphics rendering sibling. Beautiful games get a lot of coverage and attention, and developing graphics is a question of engineering. In a business plan it’s therefore rational to emphasize graphics. Both in terms of predicting the amount of effort required to implement it and the potential payoff.
Because of these severe limitations on how much processing power the intelligence is allowed, developers are forced to dumb-down the processing and make their AI appear intelligent instead.
To some it may not be clear what the difference is between making something appear intelligent and actually making it intelligent. After all, there has to be some amount of intelligence if something’s intended to keep its appearances. Right?
An intelligent system is expected to produce solutions to problems, uncertainty and often in complex situations. Appearances, however, are concerned with making an observer believe they are intelligent. To accomplish this in games the environment (the input to the AI) is kept controlled and limited. For a vivid example of this:
- NPCs in games don’t use computer vision to perceive where the player is, instead they get fed (X, Y, Z) coordinates, giving the appearances of eyesight and visual processing capabilities.
The game designers tailor the environment and its limits to make sure that the intelligent processes can handle them, and vice versa. It’s the lifelike gatekeeper who doesn’t need to know how to find his way home because he has no home. It’s the terrorist that can pull a trigger but couldn’t count his fingers if you took his shotgun and held it to his head (and you can’t).
For a different and real-life example of appearances versus actual intelligence, to show how diversified the game AI “trickery” can get:
- The developers of Halo 3 found a correlation between how smart the AI was and how tough it was. If they made the NPCs smarter the game became tougher. If they increased the health of the NPCs, making them tougher, the human players perceived them as more intelligent.
The first rule of AI in commerce is that you don’t talk about AI in commerce
Like I’ve stated in another post, the gaming industry is pretty much the only industry that dares market products using the term artificial intelligence. The reason for that is complicated and I won’t talk about it from all perspectives. But what I will tell you is that its partly due to people knowing what to expect from games.
The gaming industry builds from the sets of platform resources and devteam innovation. These are sets that the consumers know. It’s the set that the elite gamer knows because he understands the nature of programs and what the required Hz’s actually stand for. And it’s a set that the average gamer knows because he’s fought space aliens so many times that he’s learned what they’re capable of. With consumers that know what to expect, the term can be used without people boiling a can of hype.
In contrast, Academic AI builds with a set that appears to the consumer as one of infinite possibilities: Because people don’t know (exactly) how the mind works, they/we can’t evaluate how far we are from recreating it in machines. It’s unknown. And because the average consumer can’t accurately evaluate the unknown, it doesn’t matter if yesterday’s AI was primitive; most will still anticipate it advancing leap-years overnight. Just like kids in the backseat of a car asking if it’s: “human now? it must be human now? how about now?“.
Thus, when the term AI is used in areas where the limitations and previous products don’t foretell the nature of the next, people start imagining Terminators all over again. And then they get incredibly disappointed when they realize all the AI can do is answer questions about farm animals. (And only when you begin a sentence with “What is…”).
That’s obviously not a scenario a businessman would like to encounter, so it’s best just to focus on something else when marketing, like Apple doeswhen presenting its operating systems to the public.
In summary
Game AI is just one of many different subfields of AI and is governed heavily by the laws of commerce, entertainment value and modern desktop computing resources. While it may seem it must intersect with other subfields, the truth is that its a somewhat isolated field with its own sets of tricks and tools. (Mostly tricks.)
At times, games may also feel like they are the only commercial products successfully employing AI. But this is largely because of too high consumer expectations to AI in other products, and the consequent fact that companies don’t like mentioning that their product uses, what is by definition, artificial intelligence.
And finally, a difference between apparent intelligence and actual intelligence is that the latter figures out solutions to problems, while the former doesn’t care about what happens under the hood as long as an observer thinks it’s intelligent. A lot of the times—that doesn’t involve intelligence at all.
Links & references
As if it wasn’t bad enough for the military to muck about with mind control, they’re also bent on creating an online, self-teaching artificial intelligence.
Photosynth, a Microsoft Live Labs project 
Demonstrations spotlighted the wireless transmission of electrical power, dextrous robots with new sensory abilities, a direct interface to the brain, programmable materials that can be used for shape-shifting devices such as resizable cell phones, and silicon photonics that enables chips to communicate with photons rather than electrons. Jan Rabaey, a professor at the University of California-Berkeley, believes each person will have about 1,000 radios soon, most of them vanishingly small, “cognitive,” and collaborative, able to link together in a mesh network that collectively can transmit data faster, in greater quantity, more efficiently, and more reliably. Intel envisions devices constructed from tiny programmable elements called catoms (claytronics atoms). With shape-shifting technology, a mobile phone could shrink for unobtrusive storage in a pocket, then expand to a more convenient size when in use, or build a fluidly reconfigurable model of a car. Also see: 
