We have two new playtests/surveys going on, this time about strategy games. Those of us working on AI in the Center for Computer Games Research are interested in creating adaptive mechanisms for games, and automating the generation of various forms of game content. For strategy games, we are working on automatic map generation, automatic rule generation and AI techniques for competently playing strategy games with little domain information.
What we want from you is some help with evaluating the results of our recent endeavours.
The first survey is about automatically generated rulesets and general AI. If you take this survey, you will play a tutorial scenario and then two different (very short) scenarios in a strategy game you have never seen before. You will then be asked which one you prefer, which opponent AI was best, and a couple of other relevant questions. It will take you about 10 minutes. Click here to participate in the playtest survey.
You can also read the paper describing some of the previous research leading up to the system used in the current playtest. However, you are only allowed to read the paper after you've taken the survey. No cheating!
The second survey is about automatically generated maps for the classic real-time strategy game StarCraft. If you take this survey, you will be asked to look at ten different StarCraft maps and judge their relative qualities. It helps if you have played StarCraft, but it's not necessary. This survey will take you about 5 minutes to complete. Click here to take part in the StarCraft survey.
When you're finished, you could reward yourself with reading a paper on how the maps were created (though the current version of the system has evolved a bit). But only after you've taken the survey, or you'll bias the results...
Seriously though: thanks a lot for helping us with this!
Thursday, July 14, 2011
Tuesday, July 12, 2011
On the Game AI versus traditional AI debate
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.
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.
Saturday, July 09, 2011
New Infinite TD playtest
Elvis has now done significant additional development on his Infinite Tower Defense game. Please help us by playing the game and answering
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