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AI in Video Games vs. AI in Academia
missingmatterboy writes "Dr. Ian Lane Davis, AI researcher turned game development studio head, talks briefly about the differences between AI used in the game industry and the AI being researched in academic institutions. A short read but you may find it interesting."
Dragon Warrior 1 captured a girl's mind pretty well.
'Dost thou love me?'
'no'
'But thou must! Dost thou love me?'
'no'
'But thou must! Dost thou love me?'
*sigh*
'yes'
'I'm so happy!' *Cue music*
"I only speak the truth"
Karma: null(Mostly affected by an unassigned variable)
Make love, not sigs
This seems a bit much even for Wired. The creatures in these games are following a predefined set of rules, certainly they are a complex set of rules, but the way they "learn" is entirely predetermined (that is, what they learn depends on what they are exposed to, but the formula for converting exposure into knowledge is set by the game designers). I think the fact that the graphics are rendered so realistically makes it easier to make the leap to thinking they are really acting "intelligent."
Who knows what really sets human intelligence apart, is it ability to make rules or nondeterministic memory or whatever, but it seems evident (to me, in my ever-so-humble opinion) that these creatures don't have it.
- adam
Most video games I've played had a pretty simple AI algorithm:
Easy - Computer player doesn't cheat
Medium - Computer cheats and always knows where you are or what you are doing
Hard - Computer cheats and is allowed to break the rules.
If game programmers spent more time writing smart (as opposed to cheating) computer opponents and less time trying to get 10 million more polygons on the screen, todays games might actually be worth buying.
On the other hand the game industry hasn't really used a lot of the research academia has come up with. It would be really cool to see some text-to-speech stuff in games. That would probably make the dialogue in games a whole lot better.
PK
"I don't think all those AI coders out there are thrilled by the idea that their lifes work is used for games
Maybe they're thrilled, maybe they aren't. Aside from conducting interviews with the researchers themselves, we really don't have any way of knowing. That's sort of beside the point, though.
I think the simple fact of the matter is that both applications probably benefit each other, although possibly not in the way most people might think. When I started out programming, a lot of my initial projects were focused on game development. A recurring theme in my thinking was ways to make the computer opponent "smarter", which naturally led me to wonder how I could make the computer learn new tactics and adapt to the human player's actions. As I quickly learned, adaptive systems research is serious stuff.
So, I decided to dig into whatever materials I could get my hands on related to artificial intelligence research and theory. To be honest, I never really got very far, but it remains an interest of mine to this day. I'd be willing to bet some of tomorrow's leading AI researchers are playing video games today. That seems like a pretty good benefit to me.
I guess the key point is this: if a particular application of a certain technology gets people excited about it, and interested in researching it, it's a Good Thing.
Maybe he meant 2 * 10^14, which would at least only be 3 orders of magnitude off.
A much closer approximation is 100,000,000,000 neurons, and 5,000 times that many connections.
(For more on the number of neurons in the brain, see R.W. Williams and K. Herrup, Ann. Review Neuroscience, 11:423-453, 1988)
If a single neuron could perform the equivilant of an instruction, then human brains would only be 100-1000 times more powerful than a modern desktop computer, probably less when you consider that they're more like a beowolf cluster than a single powerful computer.
-- Spam Wolf, the best spam blocking vaporware yet!
When looking at AI and Cognitive research, you really have to keep in mind that there are two differwent motivations at work in doing the research.
One motivation - the one alluded to in the article - is to make stuff that gives the same behavior as humans (or whatever animal you are looking at). You don't really care whether your methods are biologially correct, you want things that work. Most of classical AI falls into this category.
The other motivation is to figure out how we do things (we being animals in general). If the research ends up being useful in appolications, great, but that's not the goal of the work. You really want models of how real brains solve problem, and these models may be far too inomplete or computationally intensive to be used in implementations, yet be perfectly fine for their intended use. A lot of Cognitive science falls into this category.
Game AI designers probably have a much richer mine of information and techniques in AI than in cognitive research, and they have so far been able to exploit that knowledge - as well as judicious 'cheating' - to make a compelling illusion. If/when they turn to cognitive science, however, the pickings will be slimmer and harder to use, as the methods and models aren't designed to solve any kind of real-world problems to begin with.
/Janne
Trust the Computer. The Computer is your friend.
Now that we've been told, yet again, how limited AI is can we take away their moderator privledges? The AIs keep moding me offtopic...damn metaphorically challenged silicone... Oh no here they come again...
heuristic algorithm seeks stochastic relationship
This posting quotes from the book to make this point.
Most households were first introduced to computers by video games. It does not surprise me that the first introduction to AI for many people is computer games. I realize that spell checking and grammer checking, a form of AI, may be in many houses too.
Even the military is using game-developed technology for combat simulators.
Vintage computer games and RPG books available. Email me if you're interested.
For example, computer vision -- there are publicly-traded companies out there which have been doing machine vision for YEARS. These systems are used by all major chip manufacturers, most major paper and textile manufacturers, etc. to catch recognize and catch defects in products before they leave the assembly line. Cognex is a $1B a year company -- they exclusively do machine vision and visual pattern recognition for industrial applications.
Another example of a company applying AI would be Virage, who has several patents relating to image/video searching and indexing.
Many investment houses use neural networks to profile and model investments, and plenty of large financials use expert systems and neural networks to for data mining, employee profiling, and so on.
Expert systems have been applied to computer security as well -- Rapid 7 (my company) sells a network security scanner which uses the Jess expert system from Sandia labs. The value of the expert system is, it allows the product to use discovered vulnerabilities to further exploit the network, discovering more vulnerabilities, which enable more probes to be performed, etc.
In a very bottom's up biological intelligence thing (animal reasoning), cruise control *can* be (I don't know if it *is) structured as an AI thing.
You have two analog controls, gas and brake. You have time; how long to break, how long to accelerate; you have intensity, or magnitude, how much gas and how much brake pressure, and then you have current velocity, current RPM, current gear, and even mass to take into account, not to mention road curvature, road quality, and road grade (steepness).
In this light, it's a very valid AI question. Can you create a system that maximizes fuel economy and ride quality (you want to avoid extreme acceleration and deceleration, right?).
I know for a fact that I can outperform my car's cruise control for both milage, performance, and ride quality. As long as I can perform better than my car, then the car isn't being intelligent enough, and is therefore an AI quality problem.
To be more precise:
If you're on a down grade and you're below the threshold speed, you can let the car coast and naturally accelerate. If you're above the threshold speed, you need to actually slow below the threshold speed to take into account the fact that there is acceleration as a factor. Or instead of braking, the car can shift into a lower gear, alternating with braking, to insure brakes don't overheat.
Then there's curvature. The car should actually decelerate going into a curve; it should do so more aggressively the tighter the curve, but as the driver starts straightening it should accelerate again. How much should it slow down? How much should it accelerate? It's not linear, but depends strongly on how banked the road is and what the road conditions are. Wet vs dry, or even icy, for example.
Or going uphill. The car should accelerate to counter the speed drop, but should probably try to stay in the best gear, even if it means falling below the threshold for a while, because of fuel economy and power output. So it should accelerate somewhat, but be able to decide that staying in 5th at 70mph isn't nearly as good as dropping to 4th and going 63mph if the grade is steep enough. It should probably also be able to check engine temperature to guage when to keep going 70mph, and when to switch to a lower gear and drop to 63mph (loong shallow grade vs small, if steeper, hill, for example)
See, right now cruise control is really only best for straight sections of clear road because not enough AI has been applied, and not enough AI is available, to deal with curvy windy uphill and downhill roads, which is actually a better place for AI to be used, allowing the driver to concentrate on where the car is going (not over the cliff, I hope)!
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