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Talking 'Bout Game AIs
Steven sent over an interview Feedmag has got with the lead AI programmer for Black & White. He talks about some of the creature/villager routinues in the game, which is interesting for the game, but also interesting in terms of how much the world of AIs for games has changed in the last few years.
is that they become predictable. Once you learn the exploits and how they work, the game is no longer fun. Take Alpha Centauri or Master of Orion 2, easily 2 of the best, if not the best, strategy games around (IMHO of course). However I can play both of them on impossible levels and win almost every time.
And what really bugs me is that to make up for deficiencies in their AI, as the levels increase in difficulty, the computer just cheats more. I was abhorred when I found out first hand how badly the AIs cheated at the higher levels in the 2 aforementioned games.
So what my question is, is this: How can this be fixed?
I have a few ideas. One is that you need one that learns. Before you flame me about this, let's think about this for a second. We're not talking about an AI here that can learn how to write a novel, we're talking about relatively straightforward strategies and mechanical play in these games. I know that 95% of of my strategy for these games is down to an art, it's just an automated system until I get to the few points at which I need to make a new decision, or something new crops up. So if I can do this by a predefined strategy, then why can't the computer do that? Keep in mind too that the computer can simply try variations on it's current strategies, and see what happens. If I beat the computer 9 out of 10 times, and one time with some wierd method the computer CLOBBERS me, then hey, maybe it should keep that method around. Also the computer can play against itself, with many different strategies, seeing how each one works. Keep in mind here folks that the strategies that I'm talking about have a few variables: how fast do I expand? at what point to I build an army? how big do I build my army? When do I stop expanding? When to I attack, and who? These can be values that can be changed and experimented with, and hence the computer could learn.
Secondly, one of the things I loved about Alpha Centauri is that just-about all settings were configurable through text files. This was amazing. You could make things easier or harder, change global settings, pollution rates, everything. You could even make new factions and trade them with your friends. If somehow settings for the AI were configurable this way, then people could learn how to tweak the AI to make it a more formidable opponent, and then share this information with others.
Combining those two ideas, throw it on the internet. If you have 5,000 people that are connected (not necessarily at the same time), you can try out hundreds of thousands of strategies for the AI to see what works well, and then upgrade the AI. Actually I think that is a necessity. The AI needs to be easily upgradable, otherwise it'll just get boring as you learn how it works and you can cream the game.
I'd love to hear some (constructive only please) comments about this, as it's been something I've been thinking about for a while.
Want to check out about the new Master of Orion 3? Awesome stuff happening there. -- Telek
If God gave us curiosity
Academia needs to make it more widely known to the software industry that stuff like this has been available.
:)
academia has been trying.
there are (at least) two big problems in migration of ideas from research into development.
1. time scales. as one developer put it, "if i want to use a new AI technique in a game, i have about two weeks to research it, and a month to implement it. any more than that, and i won't be able to justify the time spent on it to my boss."
this is pretty standard in the industry, btw. otoh, it would take a skilled ai programmer easily more than a month-and-a-half to implement and debug an inference engine in C++. and you can forget about something like writing a compiler for building behavior-based networks - that takes too much time.
2. different priorities. academic AI traditionally focuses on different things that games. in academia, working systems matter, but they're vehicles for the theories and techniques, which are the real crux of the matter. the programs can be slow, and they can consume vast resources, as long as they provide a novel insight into how human mind or human behavior works.
games, otoh, run under tight performance constraints (ie. in a 30fps game, even with 10% of cpu available to AI, you have 3.3 milliseconds per frame to do all of your AI, including collision detection and pathfinding!), and its goal is not scientific insight, but believability - the creatures can be dumb as buttons, and they can be directed by simple finite state machines, so long as they look like they're doing something cool.
with such different goals, it's not clear what can be done to bring the two closer together. for now we can just hope that if more game developers had formal training in AI techniques (as opposed to learning AI by hacking FSMs or NNs or whatever the fad-of-the-day is), and more academics were aware of constraints of the gaming industry, it would foster a better cooperation and exchange of ideas...
It works well here, but be careful claiming this is anything bigger than excellent game AI using well-known techniques.
amen to that.
My other car is a cons.