9 ms·

AI Uses Less Than Two Minutes of Videogame Footage to Recreate Game Engine

125 points by ehllo 9y ago

ehllo 9y ago
Link to the Paper: https://www.cc.gatech.edu/~riedl/pubs/ijcai17.pdf https://www.cc.gatech.edu/~riedl/pubs/ijcai17.pdf
herbstephens 9y ago
wow, impressive
ryacko 9y ago
Soon even artists will be unemployed.
- pcurve 9y ago
  I would think it's actually engineering job that is more at risk. Artists and animators will visually create these games and AI will figure out the code.
  - memco 9y ago
    One thing that bugged me about the 2009 Star Trek film was why Checkov runs over to take manual control of the teleporter to beam up Kirk as he’s freefalling towards the planet: surely a a machine at their level of technological sophistication would be able to calculate the trajectory and beam them up in real time without too much trouble.
- wpietri 9y ago
  Just the opposite, I think. The promise of computers has always been to take over boring work so that humans can focus on the more interesting bits. Artists will always have something to do.
  - cheez 9y ago
    You might need less artists or be able to create more games with the same number.
  - jdietrich 9y ago
    Entirely plausible hypothetical: what happens if Deepmind train a massive neural network on the entire Spotify catalog, weighted for Billboard chart position and Grammy nominations? What if that algorithm turns out to be as good at songwriting as AlphaGo is at the game of Go? Will anyone listen to human-written songs if AlphaSong starts producing superhumanly beautiful music? Will any of us listen to the same music if AlphaSong can analyse our personal playlists and produce an infinite number of songs that perfectly match our musical tastes?
    - YeGoblynQueenne 9y ago
      In practical terms, the problem with learning songwriting, vs learning Go, is that the number of board positions in Go is finite whereas the number of songs that can be written is infinite. With Go, then, the task of an AI player (like AlphaGo) is to search a finite space for those board states that allow it to win. With songwriting- the task is not even clearly defined. Obviously, you're trying to generate songs people will want to listen to, so you have to search an infinite space for a set of songs that satisfy some criteria of "listenability", but what criteria are these? Popular songs range over wildly different music styles, from Black Metal to Medieval folk revivalist music through RnB and Arabic belly dancing music. What examples will we train our AI songwriter on? All music, ever? Specific kinds of music? Specific songs as exemplars of specific styles? Or in other words, what exactly will our AI songwriter be trying to learn? It'd be looking into an infinite haystack filled with almost identical needles for a particular needle it's never even seen well enough to recognise.
      - jdietrich 9y ago
        >In practical terms, the problem with learning songwriting, vs learning Go, is that the number of board positions in Go is finite whereas the number of songs that can be written is infinite. There are about 10^170 legal positions in Go. We think that there are about 10^82 particles in the observable universe. The space isn't infinitely large, but it's close enough. The space of enjoyable songs is remarkably small. There are only 12 notes in the chromatic scale and 8 in the diatonic scale. The human range of hearing only covers about nine octaves. There are a relatively small pool of rhythms and harmonies that sound pleasing to most people. Accidental plagiarism is a very common problem in popular music - it's easy to unintentionally write a song that's almost identical to another song. The most common defence in music plagiarism cases is simply to find lots of prior art; the song that you've allegedly plagiarised is probably extremely similar to a lot of other songs. Jazz musicians can improvise over unfamiliar chord progressions precisely because most music isn't particularly original. If you understand music theory, you can make fairly accurate guesses about what's coming next. Practically all music follows a similar set of understandable patterns, even across cultures. https://www.theatlantic.com/science/archive/2016/09/music-plagiarism/499985/ https://www.theatlantic.com/science/archive/2016/09/music-pl...
- nawtacawp 9y ago
  For sure: "The new, modified version, Creative Adversarial Networks (CAN), is designed to generate work that does not fit the known artistic styles, thus “maximizing deviation from established styles and minimizing deviation from art distribution,” according to the paper. For the training, they used 81,449 paintings by 1,119 artists in the publicly available WikiArt data set." https://news.artnet.com/art-world/rutgers-artificial-intelligence-art-1019066 https://news.artnet.com/art-world/rutgers-artificial-intelli...
- markshead 9y ago
  As I understand it, this would allow you to create a level of a game like Super Mario Brothers as usually and then it could use that to generate additional levels that work in the same way with the same set of rules.
deleted 9y ago
[deleted]
jcranberry 9y ago
I wonder how this works for 3D game engines? It would be positively insane if AI could automate one of the most complex and high skill requirement positions in the video game industry.
- yorwba 9y ago
  What would that position be? You realize that this is about copying the mechanics of an existing game, right? You could maybe adapt it to learn from hand-animated examples, which would certainly make some games easier to create. But I doubt that the effort required to specify all interactions in a 3D game by hand would be much less than just coding them.
  - R_haterade 9y ago
    What if you trained it on footage from the real world? I didn't see anything in the article that stated the AI needed any sort of feedback from interactive controls...
    - yorwba 9y ago
      It doesn't need feedback from controls, but they have to be present as inputs during the learning process, otherwise you can't hook them up correctly. Learning real-world physics from video would be impressive in its own right, but alone it's not enough to create a game. It's also not necessary, since we can already simulate most physical phenomena; and much more efficiently than what a learning process is likely to produce at first.
    - nur0n 9y ago
      In addition to the limitations stated by sibling response, you would also need to solve the problem of image classification before you could start on real world footage; the ai in the paper was given the videogame sprites in addition to the raw footage.
- markshead 9y ago
  Interesting idea. As I understand it, you must give it a file with all the objects that will be shown in the game first. I think the general idea might be applicable to 3D but I think you'd need to give it all the objects that could appear as 3D models first.
dandermotj 9y ago
Clickbait, but great results all the same
npad 9y ago
There’s a good summary by Two Minute Papers here: https://youtu.be/2VyhmbEjs9A https://youtu.be/2VyhmbEjs9A
LeifCarrotson 9y ago
Reminds me of the moral of "that alien message": > Riemann invented his geometries before Einstein had a use for them; the physics of our universe is not that complicated in an absolute sense. A Bayesian superintelligence, hooked up to a webcam, would invent General Relativity as a hypothesis—perhaps not the dominant hypothesis, compared to Newtonian mechanics, but still a hypothesis under direct consideration—by the time it had seen the third frame of a falling apple. It might guess it from the first frame, if it saw the statics of a bent blade of grass. http://lesswrong.com/lw/qk/that_alien_message/ http://lesswrong.com/lw/qk/that_alien_message/
- asaddhamani 9y ago
  That was a very interesting read. Thanks for linking it!
- gear54rus 9y ago
  So we are looking at it from the perspective of an ai. wow. that was really cool. I did not understand the part about the internet though, what does this mean? > oh-so-carefully persuade them to give us Internet access, followed by five minutes to innocently discover their network protocols, then some trivial cracking whose only difficulty was an innocent-looking disguise. and what about AIs melting? why is that? I feel dumb but I enjoyed it:)
  - AgentME 9y ago
    Humanity is running in a simulation in the alien's world. Humanity convinces the aliens to give humanity access to the alien's world's internet, so that humanity can learn about the alien's world and to be able to talk to other (trickable) aliens. "Five minutes" refers to five minutes in the alien's world, which is thousands of years to humanity. >and what about AIs melting? why is that? The author is just lampshading the fact that he doesn't want the story to involve any (non-human) AI. The whole story is making an example about the capabilities of AI, so if humanity within the story themselves developed AI, then it would make the metaphor unnecessarily recursive.
projectramo 9y ago
Cool. Now they need to create a tool so that a person can animate a few frames deterministically to imply the physics they need. Then the AI extracts the engine, and one can use that to write the game. I am curious, though, as to what kind of concepts the game discovered or had to be taught. Was it taught platform concepts (platforms, killer objects etc) and then simply had to map to what is what on screen? Or can it really learn almost any videogame?
- yorwba 9y ago
  As is usual in such cases, it pays to look at the paper for all the details that the hype article leaves out. They use prior knowledge about the sprites that can appear in the game to extract them from the video frames. The extraction result is then represented as a list of facts (sprites present, their absolute and relative locations, velocities and the camera scroll position). Learned rules are applied to these facts to derive a representation of the next frame, which is then displayed. That means that platforms and killer objects are discovered concepts, but the features that allow their discovery (relative positions) are already hardcoded. Since most games have collision-based interactions, that is likely not much of a limitation. The greater problem will be with hidden information. If e.g. a bomb stays black for 5 seconds after being placed before it starts blinking red and then exploding, their method would be unable to learn when the bomb should start to blink, because their representation of game state has no memory. Same with objects dropped off-screen, inventory, quest status or any other information that is relevant to the game, but not always displayed on the screen.
yters 9y ago
It'd be interesting if they released the replicated levels for humans to play, as well as the level source code for examination.
jupiter90000 9y ago
Does this really learn a game engine that a human user could play the game with? I can't tell from the article or the paper. It sounds like it learns rules from how the objects are seen to move and tries to replay that from the learned rules. I'm not sure what the point of that would be, when the video it learned from already replays without error. If someone has more insight let me know what I'm missing.
- yorwba 9y ago
  The rules it learns take player input into account, so yes, it should be possible to play the replica just like the original game (modulo glitches).
  - YeGoblynQueenne 9y ago
    The video frames it learns from have no information about player input ("press X to jump"). So I don't see how it can learn a function from player inputs to game engine states- without which there's no way to actually play the game, let alone in a different way than the original.
    - yorwba 9y ago
      Maybe I'm misunderstanding the paragraph in the paper that begins with The final type of neighbor that the system handles, changes a rule from being handled normally to being considered a “control” rule. This handles the case of rules that the player makes decisions upon. After rereading it I'm no longer sure of my previous interpretation. Maybe they are generating these rules conditional on the player input without telling the training process what the input was. But they evaluate the realism of the learned engine against the original by training a reinforcement-learning agent to play the game, which means that they somehow connected those conditional rules to their corresponding input values.
      - YeGoblynQueenne 9y ago
        The way it's reported is confusing but after a second, more careful read-through, I still can't see that the "engine" they learn is anything like an ordinary game engine that can be compared by a human player's inputs. Specifically, they don't mention anywhere that their engine was played by a human- to prove its playability. Maybe that's an omission or they didn't consider it important, but it makes it very hard to figure out if this is at all possible. Finally, there's this bit on the 4th page (next to the algorithm box) where they say: "At this point we can guarantee that we have an engine that can predict the entire sequence of frames from the initial frame. Notably this means that the engine can only reflect changes that actually occur in the input sequence of parsed frames". I take this to mean that, given a specific starting frame, their engine wil always predict the same sequence- which must mean its prediction does not take into account player input. But it's true that this point is very hard to figure out. >> Maybe they are generating these rules conditional on the player input without telling the training process what the input was. Basically, yeah- that's my understanding.
juskrey 9y ago
For me, gaming was all about secrets. Obviously pattern detector can't replicate that..
dvt 9y ago
This is so misleading, it's not even funny. Predicting animation frames is something completely different than recreating a game engine. It's the difference between re-creating a movie frame-by-frame and building a movie camera. Obviously this is impressive in its own right, but we're a a long ways away from AI building general-purpose systems.
- yters 9y ago
  The constant overselling of AI is a negative indicator.
  - jdietrich 9y ago
    I'd suggest that you review how computing was reported on in decades past. We made a lot of predictions that seemed utterly fantastical for some decades, but eventually came good when Moore's Law caught up. Many of these predictions drastically underestimated the impact of computing, particularly with regards to networking and mobile. I sincerely believe that the move towards deep learning is every bit as radical as the development of the microprocessor. We're starting to find incredibly elegant solutions to problems that have stymied computer scientists for decades. We're finding orders-of-magnitude improvements to difficult problems at an astonishing rate, often with remarkably modest resources. There's clearly a huge amount of hype happening at the moment, but genuine technological revolutions are usually preceded by a ludicrous hype bubble. https://www.youtube.com/watch?v=HW5Fvk8FNOQ https://www.youtube.com/watch?v=HW5Fvk8FNOQ
    - yters 9y ago
      I know no one wants to be that guy who said we only need 64k, but hindsight is 20/20. The DL hype is driven more by the singularitians than by actual usefulness of DL. The personal computer revolution was driven by the fact personal computers are very useful.
    - YeGoblynQueenne 9y ago
      >> We're starting to find incredibly elegant solutions to problems that have stymied computer scientists for decades. I think by "incredibly elegant solutions" you mean the typical black-box statistical machine learning algorithm that takes in some data as input and outputs an approximated function that "solves" whatever problem your data was representing. If that is indeed what you mean (apologies otherwise) I really struggle to see that as "elegant" -or even a solution. We start with some problem too complex to understand and build a complex system we don't quite understand, that solves the problem in a way we don't quite understand. What did we gain? A system that solves the problem - maybe, when it feels like it, depending on the problem, provided enough data etc etc. Our understanding of the problem hasn't changed and therefore our ability to solve it, hasn't. To give an analogy- it's like a kid at school who can't solve some arithmetic problem they have for homework asking their big sister to solve it for them. The kid now knows the answer to the problem but still doesn't know how to solve it, on their own. They didn't "find a solution"- they found someone who knows the solution. I think, if you look at the progress we've made as a civilisation in producing knowledge, you'll find that this was always driven by people who solved problems, themselves. And if you think of the kind of people who invoke some higher authority that has all the solutions, you're probably looking at religion.
ketsa 9y ago
very misleading title...
yters 9y ago
If frame prediction counts as a game engine, then I can write a game engine by recording myself play call of duty.
YeGoblynQueenne 9y ago
The definition of "game engine" in the paper is subtly different to the one most players and game engine programmers would expect. The paper calls the set of game mechanics a "game engine". I think the players and programmers would probably call that "mechanics" and refer to the actual code running the game as an "engine". For example, if I think of an AI that "learns a game engine from video" I would expect to see an AI that can take as input video of, say, myself playing XCOM2 and output the Unreal engine (or an engine with an interface indistinguishable from it). In other words, I'd expect to see an AI that goes from example game output to an automaton that can reproduce that output _and any other output that the original automaton could produce_. Instead, from a quick read of the paper, I understand they learn to reproduce a set of video frames that match the input- and then train a different AI to actually simulate the physics (i.e. make sure the player doesn't fall through walls etc). While this is remarkable and I'm very glad to see a rule-based, greedy approach that seems to work pretty damn well, I really don't think it does what it says on the tin. Still- this was published in IJCAI. So kudos to the authors for that.
davidmanescu 9y ago
In the article they've trained the AI to predict what frames the game would provide given a certain input, but to demonstrate its effectiveness they show the original game given a certain input, side-by-side with the AI's prediction given the same input. I sure hope that wasn't part of the training set. Teaching an AI to recognise something it's already been taught and mirror it relatively closely isn't an achievement...
GrumpyNl 9y ago
There is money in AI so everybody claims to be in AI right now. Most claims i have seen so fare are smart list pickers.
infinity0 9y ago
The article doesn't explain why it's not overfitting. Overfitting isn't that impressive.
psyc 9y ago
I recall Eliezer Yudkowsky once writing that a superintelligence could deduce special relativity from 2 seconds of footage of an apple falling to the ground.

125 points by ehllo 9y ago

ehllo 9y ago

Link to the Paper: https://www.cc.gatech.edu/~riedl/pubs/ijcai17.pdf https://www.cc.gatech.edu/~riedl/pubs/ijcai17.pdf

herbstephens 9y ago

wow, impressive

ryacko 9y ago

Soon even artists will be unemployed.

pcurve 9y ago
I would think it's actually engineering job that is more at risk. Artists and animators will visually create these games and AI will figure out the code.
- memco 9y ago
  One thing that bugged me about the 2009 Star Trek film was why Checkov runs over to take manual control of the teleporter to beam up Kirk as he’s freefalling towards the planet: surely a a machine at their level of technological sophistication would be able to calculate the trajectory and beam them up in real time without too much trouble.
wpietri 9y ago
Just the opposite, I think. The promise of computers has always been to take over boring work so that humans can focus on the more interesting bits. Artists will always have something to do.
- cheez 9y ago
  You might need less artists or be able to create more games with the same number.
- jdietrich 9y ago
  Entirely plausible hypothetical: what happens if Deepmind train a massive neural network on the entire Spotify catalog, weighted for Billboard chart position and Grammy nominations? What if that algorithm turns out to be as good at songwriting as AlphaGo is at the game of Go? Will anyone listen to human-written songs if AlphaSong starts producing superhumanly beautiful music? Will any of us listen to the same music if AlphaSong can analyse our personal playlists and produce an infinite number of songs that perfectly match our musical tastes?
  - YeGoblynQueenne 9y ago
    In practical terms, the problem with learning songwriting, vs learning Go, is that the number of board positions in Go is finite whereas the number of songs that can be written is infinite. With Go, then, the task of an AI player (like AlphaGo) is to search a finite space for those board states that allow it to win. With songwriting- the task is not even clearly defined. Obviously, you're trying to generate songs people will want to listen to, so you have to search an infinite space for a set of songs that satisfy some criteria of "listenability", but what criteria are these? Popular songs range over wildly different music styles, from Black Metal to Medieval folk revivalist music through RnB and Arabic belly dancing music. What examples will we train our AI songwriter on? All music, ever? Specific kinds of music? Specific songs as exemplars of specific styles? Or in other words, what exactly will our AI songwriter be trying to learn? It'd be looking into an infinite haystack filled with almost identical needles for a particular needle it's never even seen well enough to recognise.
    - jdietrich 9y ago
      >In practical terms, the problem with learning songwriting, vs learning Go, is that the number of board positions in Go is finite whereas the number of songs that can be written is infinite. There are about 10^170 legal positions in Go. We think that there are about 10^82 particles in the observable universe. The space isn't infinitely large, but it's close enough. The space of enjoyable songs is remarkably small. There are only 12 notes in the chromatic scale and 8 in the diatonic scale. The human range of hearing only covers about nine octaves. There are a relatively small pool of rhythms and harmonies that sound pleasing to most people. Accidental plagiarism is a very common problem in popular music - it's easy to unintentionally write a song that's almost identical to another song. The most common defence in music plagiarism cases is simply to find lots of prior art; the song that you've allegedly plagiarised is probably extremely similar to a lot of other songs. Jazz musicians can improvise over unfamiliar chord progressions precisely because most music isn't particularly original. If you understand music theory, you can make fairly accurate guesses about what's coming next. Practically all music follows a similar set of understandable patterns, even across cultures. https://www.theatlantic.com/science/archive/2016/09/music-plagiarism/499985/ https://www.theatlantic.com/science/archive/2016/09/music-pl...
nawtacawp 9y ago
For sure: "The new, modified version, Creative Adversarial Networks (CAN), is designed to generate work that does not fit the known artistic styles, thus “maximizing deviation from established styles and minimizing deviation from art distribution,” according to the paper. For the training, they used 81,449 paintings by 1,119 artists in the publicly available WikiArt data set." https://news.artnet.com/art-world/rutgers-artificial-intelligence-art-1019066 https://news.artnet.com/art-world/rutgers-artificial-intelli...
markshead 9y ago
As I understand it, this would allow you to create a level of a game like Super Mario Brothers as usually and then it could use that to generate additional levels that work in the same way with the same set of rules.

deleted 9y ago

[deleted]

jcranberry 9y ago

I wonder how this works for 3D game engines? It would be positively insane if AI could automate one of the most complex and high skill requirement positions in the video game industry.

yorwba 9y ago
What would that position be? You realize that this is about copying the mechanics of an existing game, right? You could maybe adapt it to learn from hand-animated examples, which would certainly make some games easier to create. But I doubt that the effort required to specify all interactions in a 3D game by hand would be much less than just coding them.
- R_haterade 9y ago
  What if you trained it on footage from the real world? I didn't see anything in the article that stated the AI needed any sort of feedback from interactive controls...
  - yorwba 9y ago
    It doesn't need feedback from controls, but they have to be present as inputs during the learning process, otherwise you can't hook them up correctly. Learning real-world physics from video would be impressive in its own right, but alone it's not enough to create a game. It's also not necessary, since we can already simulate most physical phenomena; and much more efficiently than what a learning process is likely to produce at first.
  - nur0n 9y ago
    In addition to the limitations stated by sibling response, you would also need to solve the problem of image classification before you could start on real world footage; the ai in the paper was given the videogame sprites in addition to the raw footage.
markshead 9y ago
Interesting idea. As I understand it, you must give it a file with all the objects that will be shown in the game first. I think the general idea might be applicable to 3D but I think you'd need to give it all the objects that could appear as 3D models first.

dandermotj 9y ago

Clickbait, but great results all the same

npad 9y ago

There’s a good summary by Two Minute Papers here: https://youtu.be/2VyhmbEjs9A https://youtu.be/2VyhmbEjs9A

LeifCarrotson 9y ago

Reminds me of the moral of "that alien message": > Riemann invented his geometries before Einstein had a use for them; the physics of our universe is not that complicated in an absolute sense. A Bayesian superintelligence, hooked up to a webcam, would invent General Relativity as a hypothesis—perhaps not the dominant hypothesis, compared to Newtonian mechanics, but still a hypothesis under direct consideration—by the time it had seen the third frame of a falling apple. It might guess it from the first frame, if it saw the statics of a bent blade of grass. http://lesswrong.com/lw/qk/that_alien_message/ http://lesswrong.com/lw/qk/that_alien_message/

asaddhamani 9y ago
That was a very interesting read. Thanks for linking it!
gear54rus 9y ago
So we are looking at it from the perspective of an ai. wow. that was really cool. I did not understand the part about the internet though, what does this mean? > oh-so-carefully persuade them to give us Internet access, followed by five minutes to innocently discover their network protocols, then some trivial cracking whose only difficulty was an innocent-looking disguise. and what about AIs melting? why is that? I feel dumb but I enjoyed it:)
- AgentME 9y ago
  Humanity is running in a simulation in the alien's world. Humanity convinces the aliens to give humanity access to the alien's world's internet, so that humanity can learn about the alien's world and to be able to talk to other (trickable) aliens. "Five minutes" refers to five minutes in the alien's world, which is thousands of years to humanity. >and what about AIs melting? why is that? The author is just lampshading the fact that he doesn't want the story to involve any (non-human) AI. The whole story is making an example about the capabilities of AI, so if humanity within the story themselves developed AI, then it would make the metaphor unnecessarily recursive.

projectramo 9y ago

Cool. Now they need to create a tool so that a person can animate a few frames deterministically to imply the physics they need. Then the AI extracts the engine, and one can use that to write the game. I am curious, though, as to what kind of concepts the game discovered or had to be taught. Was it taught platform concepts (platforms, killer objects etc) and then simply had to map to what is what on screen? Or can it really learn almost any videogame?

yorwba 9y ago
As is usual in such cases, it pays to look at the paper for all the details that the hype article leaves out. They use prior knowledge about the sprites that can appear in the game to extract them from the video frames. The extraction result is then represented as a list of facts (sprites present, their absolute and relative locations, velocities and the camera scroll position). Learned rules are applied to these facts to derive a representation of the next frame, which is then displayed. That means that platforms and killer objects are discovered concepts, but the features that allow their discovery (relative positions) are already hardcoded. Since most games have collision-based interactions, that is likely not much of a limitation. The greater problem will be with hidden information. If e.g. a bomb stays black for 5 seconds after being placed before it starts blinking red and then exploding, their method would be unable to learn when the bomb should start to blink, because their representation of game state has no memory. Same with objects dropped off-screen, inventory, quest status or any other information that is relevant to the game, but not always displayed on the screen.

yters 9y ago

It'd be interesting if they released the replicated levels for humans to play, as well as the level source code for examination.

jupiter90000 9y ago

Does this really learn a game engine that a human user could play the game with? I can't tell from the article or the paper. It sounds like it learns rules from how the objects are seen to move and tries to replay that from the learned rules. I'm not sure what the point of that would be, when the video it learned from already replays without error. If someone has more insight let me know what I'm missing.

yorwba 9y ago
The rules it learns take player input into account, so yes, it should be possible to play the replica just like the original game (modulo glitches).
- YeGoblynQueenne 9y ago
  The video frames it learns from have no information about player input ("press X to jump"). So I don't see how it can learn a function from player inputs to game engine states- without which there's no way to actually play the game, let alone in a different way than the original.
  - yorwba 9y ago
    Maybe I'm misunderstanding the paragraph in the paper that begins with The final type of neighbor that the system handles, changes a rule from being handled normally to being considered a “control” rule. This handles the case of rules that the player makes decisions upon. After rereading it I'm no longer sure of my previous interpretation. Maybe they are generating these rules conditional on the player input without telling the training process what the input was. But they evaluate the realism of the learned engine against the original by training a reinforcement-learning agent to play the game, which means that they somehow connected those conditional rules to their corresponding input values.
    - YeGoblynQueenne 9y ago
      The way it's reported is confusing but after a second, more careful read-through, I still can't see that the "engine" they learn is anything like an ordinary game engine that can be compared by a human player's inputs. Specifically, they don't mention anywhere that their engine was played by a human- to prove its playability. Maybe that's an omission or they didn't consider it important, but it makes it very hard to figure out if this is at all possible. Finally, there's this bit on the 4th page (next to the algorithm box) where they say: "At this point we can guarantee that we have an engine that can predict the entire sequence of frames from the initial frame. Notably this means that the engine can only reflect changes that actually occur in the input sequence of parsed frames". I take this to mean that, given a specific starting frame, their engine wil always predict the same sequence- which must mean its prediction does not take into account player input. But it's true that this point is very hard to figure out. >> Maybe they are generating these rules conditional on the player input without telling the training process what the input was. Basically, yeah- that's my understanding.

juskrey 9y ago

For me, gaming was all about secrets. Obviously pattern detector can't replicate that..

dvt 9y ago

This is so misleading, it's not even funny. Predicting animation frames is something completely different than recreating a game engine. It's the difference between re-creating a movie frame-by-frame and building a movie camera. Obviously this is impressive in its own right, but we're a a long ways away from AI building general-purpose systems.

yters 9y ago
The constant overselling of AI is a negative indicator.
- jdietrich 9y ago
  I'd suggest that you review how computing was reported on in decades past. We made a lot of predictions that seemed utterly fantastical for some decades, but eventually came good when Moore's Law caught up. Many of these predictions drastically underestimated the impact of computing, particularly with regards to networking and mobile. I sincerely believe that the move towards deep learning is every bit as radical as the development of the microprocessor. We're starting to find incredibly elegant solutions to problems that have stymied computer scientists for decades. We're finding orders-of-magnitude improvements to difficult problems at an astonishing rate, often with remarkably modest resources. There's clearly a huge amount of hype happening at the moment, but genuine technological revolutions are usually preceded by a ludicrous hype bubble. https://www.youtube.com/watch?v=HW5Fvk8FNOQ https://www.youtube.com/watch?v=HW5Fvk8FNOQ
  - yters 9y ago
    I know no one wants to be that guy who said we only need 64k, but hindsight is 20/20. The DL hype is driven more by the singularitians than by actual usefulness of DL. The personal computer revolution was driven by the fact personal computers are very useful.
  - YeGoblynQueenne 9y ago
    >> We're starting to find incredibly elegant solutions to problems that have stymied computer scientists for decades. I think by "incredibly elegant solutions" you mean the typical black-box statistical machine learning algorithm that takes in some data as input and outputs an approximated function that "solves" whatever problem your data was representing. If that is indeed what you mean (apologies otherwise) I really struggle to see that as "elegant" -or even a solution. We start with some problem too complex to understand and build a complex system we don't quite understand, that solves the problem in a way we don't quite understand. What did we gain? A system that solves the problem - maybe, when it feels like it, depending on the problem, provided enough data etc etc. Our understanding of the problem hasn't changed and therefore our ability to solve it, hasn't. To give an analogy- it's like a kid at school who can't solve some arithmetic problem they have for homework asking their big sister to solve it for them. The kid now knows the answer to the problem but still doesn't know how to solve it, on their own. They didn't "find a solution"- they found someone who knows the solution. I think, if you look at the progress we've made as a civilisation in producing knowledge, you'll find that this was always driven by people who solved problems, themselves. And if you think of the kind of people who invoke some higher authority that has all the solutions, you're probably looking at religion.

ketsa 9y ago

very misleading title...

yters 9y ago

If frame prediction counts as a game engine, then I can write a game engine by recording myself play call of duty.

YeGoblynQueenne 9y ago

The definition of "game engine" in the paper is subtly different to the one most players and game engine programmers would expect. The paper calls the set of game mechanics a "game engine". I think the players and programmers would probably call that "mechanics" and refer to the actual code running the game as an "engine". For example, if I think of an AI that "learns a game engine from video" I would expect to see an AI that can take as input video of, say, myself playing XCOM2 and output the Unreal engine (or an engine with an interface indistinguishable from it). In other words, I'd expect to see an AI that goes from example game output to an automaton that can reproduce that output _and any other output that the original automaton could produce_. Instead, from a quick read of the paper, I understand they learn to reproduce a set of video frames that match the input- and then train a different AI to actually simulate the physics (i.e. make sure the player doesn't fall through walls etc). While this is remarkable and I'm very glad to see a rule-based, greedy approach that seems to work pretty damn well, I really don't think it does what it says on the tin. Still- this was published in IJCAI. So kudos to the authors for that.

davidmanescu 9y ago

In the article they've trained the AI to predict what frames the game would provide given a certain input, but to demonstrate its effectiveness they show the original game given a certain input, side-by-side with the AI's prediction given the same input. I sure hope that wasn't part of the training set. Teaching an AI to recognise something it's already been taught and mirror it relatively closely isn't an achievement...

GrumpyNl 9y ago

There is money in AI so everybody claims to be in AI right now. Most claims i have seen so fare are smart list pickers.

infinity0 9y ago

The article doesn't explain why it's not overfitting. Overfitting isn't that impressive.

psyc 9y ago

I recall Eliezer Yudkowsky once writing that a superintelligence could deduce special relativity from 2 seconds of footage of an apple falling to the ground.