The Quest for AI Game Designers | AI and Games #13

Key Takeaways

[Music] Hey folks, let's start with a we heads up. This video is a reproduction of a talk held at the Game City Festival in Nottingham, UK back in October of 2015. So, we've put the original talk back together along with some cool new stuff in there, too. It's sort of like a director's cut, except I'm not fleecing you for more money, given we're delivering slightly more content to you in a more prestige [Music] format. Anyway, on with the show. It was previously thought back in the early 2000s that the internet was built for people to discuss movies in a highly negative fashion and share explicit material with each other. But man, were those kids wrong. If anything, the internet now thrives largely in the ability to discuss, market, complain, distribute, and about some more all sorts of digital media, but especially games. Now, the discussions on games can range from publishers to developers, creators, critics, and more. But let's focus on something specific. We love talking about design. People go on the social network thingies to discuss games they love and games they hate. Sometimes the reasoning behind it is really subtle, painfully specific, or deeply personal. But the thing is that we personally know good video game design when we see it. Now, the idea of something being good is really hard to determine, and we'll come back to that, but it's really the brunt of where our discussions are focused. This makes for all the more interesting discussion given that creating good games isn't exactly easy. While defining something as good or bad can prove controversial since one person's idea of good might be another's idea of bad. There's also the issue that there's no guide book, no recipe for making well-designed games. It's a very timeconuming process and often requires a lot of attention to detail, extensive play testing, gathering feedback from your testers, and scrutinizing even the tiniest of aspects behind it. So, returning to our earlier point, we're often quick to identify a good game when it's placed in front of us. But trying to explain in detail why that game is welldesigned can prove really taxing. Even when much of the technical challenge is removed, building fun and interesting games is still immensely difficult. A fun contemporary example is that found in Nintendo's 2015 release Super Mario Maker, which allows you to build Mario levels using a remarkably flexible set of tools. If you were to peruse the millions of levels now built in Super Mario Maker, you'd quickly realize that a lot of them are not that interesting or even very good. We don't mean to discourage or upset anyone who's made a level in Mario Maker, but let's be honest, most of them are pretty terrible Mario levels. Now, note that little distinction I just made. Terrible Mario levels, meaning levels akin to the design principles of Mario games. Sure, there are plenty of interesting levels doing some really amazing things, ranging from playing music to completely automating the gameplay experience. And don't get me started on Kao Mario. Seriously, just look it up. But how many are good Mario levels? In my opinion, there's a rather interesting phenomenon happening in Mario Maker and that we now have the tools to make our own Mario games. We've decided to completely mix it up and do things that are subversive or simply anarchctic. But thing is, is anyone making levels designed to replicate or instill the design principles of classic Mario games? Given the nature of how the community bubbles to the top the more sensationalist and crazy content, would you even know it exists? Now, the real problem, if you will, that exists underneath all of this is that we don't really know what Mario levels are. Like I said earlier, there's no recipe for well-designed games. And as far as we know, there's no recipe for good Mario levels that Nintendo themselves have publicly expressed. But Tommy, I hear you cry. What's all this got to do with artificial intelligence? Well, I'm glad you asked. See, everything I've just been talking about is highly subjective. In other words, what I might think is a terrible game or level you might love, and indeed vice versa. There are means by which we can express our opinions. But there are no formal metrics for determining whether a game is good or not. And if anyone says there are, they should be shot out of a cannon, preferably into the sun. So, this proves really difficult. or a more appropriate term would be interesting for artificial intelligence research. Why is that? Well, for one thing, we're used to dealing with metrics or really just any means by which to quantify progress or success in a given problem. If you go and watch the AI 101 series, I talk a lot about rewards and how they influence decision-m. So, building AI systems that solve specific tasks require means by which to quantify whether or not we're going to succeed at that problem. If we were to program say a navigation system for example, we would need means by which to express how close we are to our destination and also things like resources, you know, energy cost needed to reach that location. That way we can ensure the AI crosses that distance as fast and as efficiently as possible. This is all of particular relevance for what we call PCG, procedural content generation. BCG is, in broad academic speak, the study of software algorithms that create content with respect to a particular problem domain using some form of intelligent process with potentially some pseudo random number generation thrown in for good measure. In more layman's terms, it's an algorithm that makes something. That something could be a level, a non-player character, a weapon, a piece of armor, a story line, or an entire quest within a game. Making intelligent decisions with respect to the game state, these systems craft new content that is expected to operate within and adhere to the rules of the game world. But here in lies the more interesting problem in PCG research, ensuring that procedurally generated content is welldesigned. How do we know if a generated weapon is cool or if the level I made was fun? I simply don't know that. I can perhaps quantify whether that object is functional and that the player can navigate the level without dying or the weapon can actually be used to defeat enemies. But the quality of these items is subjective. This is where many games that adopt procedural content generation adopt different approaches to resolving the issue of quality. In cases such as level creation, see Minecraft and the like. The game constrains the types of levels that can be made. Meanwhile, in Borderlands, the guns are designed to be disposable. If the gun I just found is rubbish, that's all right. I can either throw it away, trade it, or sell it in the knowledge that I'm bound to find another one soon. [Music] So, bringing all this back to point, AIdriven game design is an excellent problem space for researchers and developers to explore because it's one of many interesting problems that exist where we can't formally define the quality of the systems answers. We can have this super intelligent level create an AI algorithm, but unlike most problem areas where we are merely interested in whether the answers are functionally correct, we have to consider whether or not this will elicit emotional reactions within players. As a result, there's a growing body of research focused on procedural content generation. This takes a number of interesting avenues and ensuring that the content that's being created is well-designed. In academic literature, we typically consider whether the generative system is adopting a theory or datadriven approach. In the former, we're making assumptions beforehand of how our content should be crafted. This is quite a common method given that we typically want procedurally generated content to adhere to specific rules of the world. However, it can often be quite limiting since we're injecting bias before any content is crafted about what is good or bad. The alternative, the datadriven method, is more reliant upon player modeling, an entire research field in itself, given we gather data while players are playing the game about how they interact with the content being built. There is also the issue of whether we allow players to be aware of and indeed interact with the generator while it is active. As we'll see shortly, this can lead to some interesting results. Furthermore, the means by which we build the content varies wildly throughout the field with a lot of work still adhering to what we would call a constructivist approach whereby content is made and potentially tested before unleashed upon players. Despite this, there's a growing body of work now adopting machine learning algorithms that evolve, build, and provide content as a continuous and adaptive system. Now, many people will be familiar with examples of PCG from both AAA and indie video games, but as said, it's an increasingly popular area of academic research. Perhaps the most popular example would be the Mario AI competition, something we've talked about before here on AI and Games, and it's a pretty popular video of ours. The competition ran for a couple of years and was focused on creating interesting Mario levels in the eyes of judges. This resulted in a variety of different approaches that relied on either established chunks of gameplay being stitched together in an intelligent fashion or through the use of metrics and huristics that were defined by the person who created the system. An example of this is Ben Weber's submission which built each level in a multi-stage process working through backgrounds, platforms, bricks, coins, and enemies individually. While the competition is sits ended, the work continues on in various forms with most recent work by Steve Dalskcog experimenting in design patterns to build levels up through the creation of individual slices to work by Matthew Guzdile who's using YouTube longplays to learn about Mario level construction. In many respects, the focus is less on whether a Mario level can be made, but whether AI can now tap into that secret unspoken Mario formula. Another well-known area of research was the indie game Galactic Arms Race, developed by researchers and students at the University of Central Florida. In GAR, particle weapons for a space combat game are procedurally generated using a learning algorithm tailored to players tastes. In other words, if you keep firing the gun, we think you not only like it, but would like new guns based upon it. We already have a full video of this on AI games that goes into it in a lot more detail. But the thing is, there's so much more happening in this field that we don't even mention here in that much detail. This ranges from generating text adventure puzzles using open world data, the creation of dungeons and their mazes complete with monsters and treasures, procedurally generating enemy AI using modular components, even to building fully playable Legend of Zelda dungeons. Much of this work is still ongoing and is still being developed academic institutions all over the world. No doubt we'll come back to talk about some of these in future videos. Now, in all of these cases, we're looking at the creation of content within an established game, such as the levels of Spalunky or planets within No Man's Sky or the weapons of Borderlands and Destiny. Some research spans far beyond just creating parts of a game. Instead, it looks to build the entire game itself. Automated game design is a form of computational creativity research. A field of study that's part computer science and kind of part psychology as AI systems are built designed either to adopt or be influenced by the creative processes of humans. A popular example is the Angelina system developed by Mike Cook. Angelina is a continually developing project focused on automated design. It can create a variety of different games in the Unity game engine. One of its most famous games created is to that sect, a game made for the Ludum Dare Game Jam. The games that Angelina create are arguably not as rich as many similar games on the market. But hey, cut it some slack. There's science happening up in here. This thing makes video games entirely by itself, don't you know. Indeed, much of the discussion around Angelina is focused not just on what it can create, but whether it could be deemed to have any form of artistic license. All in all, this is just a very brief overview of just some of the PCG research that's been going on in the last, say, 10 years. Now, this video could easily be four, five, 10 times its current length if I wanted to talk about everything that's happening out there. But this video is really just focused on explaining what the fuss is all about for us researcher types. With that, we'll wrap it up with this video, but don't forget to hit the blog where we actually go into a bit more detail about some more specific examples in this area. Thanks for watching. Don't forget to like and subscribe for more AI and games goodness. You're a member of the elite counterterrorist group Rainbow Team. Why are we called Rainbow? Well, for one thing, it gives our enemies a false sense of security as they probably expect Zippy, George, and Flipping Bongo to come bursting through that door. And they were puppets. We're not puppies, mate.