3 ms·

But if it can predict animation frames perfectly (it cannot), it is practically a game engine (for that particular game). Think of the potential: we could crea

But if it can predict animation frames perfectly (it cannot), it is practically a game engine (for that particular game). Think of the potential: we could create games just by giving the AI some example game play animations and it would learn the logic of the game (game engine).

zeusk 9y ago
> But if it can predict animation frames perfectly (it cannot), it is practically a game engine (for that particular game). No, it isn't - just like the subject in the Chinese room experiment cannot understand Chinese yet is able to reply with perfect language to given input.
- kyberias 9y ago
  > No, it isn't - just like the subject in the Chinese room experiment cannot understand Chinese yet is able to reply with perfect language to given input. Oh, come on. This is silly semantics. Human written engine doesn't "understand" the game state any more than an AI does! Game engine here _merely_ means the algorithm that takes user input, calculates the next state of the game and outputs it to the screen. No-one has claimed that this engine would be a generic engine capable of running a variety of games. No. Only this game. Whether the engine is written manually by a human or is a trained AI is irrelevant. It's still a game engine. The AI trained may have a different internal representation of the game state, but it still must have something like that. The whole POINT of this exercise is to find an automatic way of generating this algorithm without manually writing code.
  - pqh 9y ago
    I suppose, continuing this thought experiment, the generic game engine behind the AI-generated game would be the AI generating games.
  - zeusk 9y ago
    > Oh, come on. This is silly semantics. Human written engine doesn't "understand" the game state any more than an AI does! You don't seem to understand the difference between predicting what could be next compared to actually producing things from scratch. Think of this in terms of a movie where there is no user input and the state is linear (state is frame number and increments at a constant rate). Predicting next frame and saying that AI can recreate a movie is false and very misleading. Add in user input and non-linear state and your AI isn't very useful anymore, even with predicting next frames (you'll never reach perfect reproduction given the vast game state that is possible).
    - kyberias 9y ago
      You keep bringing up these useless analogues about movies and chinese boxes. Be more specific. There specifically IS user input involved here. There is no "building from scratch". I can look at (simple) video footage of a game I've never seen before and start to model in my head various ways how certain mechanisms can be implemented in code. I quickly learn to predict how different game elements move based on how they look and similarly looking things have moved before. Even if I didn't know anything about programming, I could learn to predict how that monster moves. In fact, playing the game trains my brain to predict these things. _Obviously_ I might not know what's behind that next monster. I don't know what the game designers have come up with: some things are _impossible_ to predict, but the basic mechanics can be learned and the more I play, the better my prediction accuracy gets. The mechanics of me learning these things is not too far away from e.g. neural network learning the same things. Again, obviously, the neural network cannot create the parts of the game world it hasn't seen. But no-one is claiming that. Nor that just by showing a little bit of game play, we could re-create the whole game. Only that it can probably learn to render the mechanics (game engine) based on user input. The authors showed some interesting results based on this.
      - zeusk 9y ago
        >> No, it isn't - just like the subject in the Chinese room experiment cannot understand Chinese yet is able to reply with perfect language to given input. > Oh, come on. This is silly semantics. That is the exact point of the thought experiment, semantics vs syntax. A computer can work well with syntax but semantics are not so easy. > You keep bringing up these useless analogues about movies and chinese boxes. Be more specific. They're useless to you because you don't seem to understand how neural network or current AI tech works. In the current fashion, generalist AI like a game engine is very much impossible. > I can look at (simple) video footage of a game I've never seen before and start to model in my head various ways how certain mechanisms can be implemented in code. I quickly learn to predict how different game elements move based on how they look and similarly looking things have moved before. Even if I didn't know anything about programming, I could learn to predict how that monster moves. In fact, playing the game trains my brain to predict these things. Sadly our computers don't have a human brain, which is entirely the point of AI. > The mechanics of me learning these things is not too far away from e.g. neural network learning the same things. Again, obviously, the neural network cannot create the parts of the game world it hasn't seen. But no-one is claiming that. Nor that just by showing a little bit of game play, we could re-create the whole game. Only that it can probably learn to render the mechanics (game engine) based on user input. The authors showed some interesting results based on this. Game states are too large to be entirely seen even by a computer. We need pruning and MCTS for simple adversarial searches in games like chess, Go; What makes you think some magical AI can imitate a game engine?
YeGoblynQueenne 9y ago
I think the acid test for a "game engine" should be the ability to use it to produce behaviour other than the original. For instance, if you somehow learn a Super Mario engine, you should be able to reprogram it to play a new level that you designed yourself. Even more importantly- you should be able to play the game and get a different outcome every time. From the paper I think the "game engine" they learn is not even capable of playing the same level in a different way than in the examples. If Mario jumps in frame X, the "game engine" will always show Mario jumping in frame X, and not know there's any other possible state at that time than frame X with Mario jumping in it. I mean, I see the value in this, totally- they've learned an automaton that can faithfully reproduce its examples and that's no mean feat. But like I say above it would make a lot more sense to define a "game engine" as an automaton that can reproduce the entire range of behaviours of its original- not just a set of its output frames and only that set of frames, in perpetuity. Of course a "game engine" in the sense I give it above is (in the case of games) a Turing complete automaton- so it would be impossible to learn to simulate it just from examples of its output (there's a lot of maths on that). So the paper settles for the easiest task. OK. But the way they present it is misleading.

zeusk 9y ago

> But if it can predict animation frames perfectly (it cannot), it is practically a game engine (for that particular game). No, it isn't - just like the subject in the Chinese room experiment cannot understand Chinese yet is able to reply with perfect language to given input.

kyberias 9y ago
> No, it isn't - just like the subject in the Chinese room experiment cannot understand Chinese yet is able to reply with perfect language to given input. Oh, come on. This is silly semantics. Human written engine doesn't "understand" the game state any more than an AI does! Game engine here _merely_ means the algorithm that takes user input, calculates the next state of the game and outputs it to the screen. No-one has claimed that this engine would be a generic engine capable of running a variety of games. No. Only this game. Whether the engine is written manually by a human or is a trained AI is irrelevant. It's still a game engine. The AI trained may have a different internal representation of the game state, but it still must have something like that. The whole POINT of this exercise is to find an automatic way of generating this algorithm without manually writing code.
- pqh 9y ago
  I suppose, continuing this thought experiment, the generic game engine behind the AI-generated game would be the AI generating games.
- zeusk 9y ago
  > Oh, come on. This is silly semantics. Human written engine doesn't "understand" the game state any more than an AI does! You don't seem to understand the difference between predicting what could be next compared to actually producing things from scratch. Think of this in terms of a movie where there is no user input and the state is linear (state is frame number and increments at a constant rate). Predicting next frame and saying that AI can recreate a movie is false and very misleading. Add in user input and non-linear state and your AI isn't very useful anymore, even with predicting next frames (you'll never reach perfect reproduction given the vast game state that is possible).
  - kyberias 9y ago
    You keep bringing up these useless analogues about movies and chinese boxes. Be more specific. There specifically IS user input involved here. There is no "building from scratch". I can look at (simple) video footage of a game I've never seen before and start to model in my head various ways how certain mechanisms can be implemented in code. I quickly learn to predict how different game elements move based on how they look and similarly looking things have moved before. Even if I didn't know anything about programming, I could learn to predict how that monster moves. In fact, playing the game trains my brain to predict these things. _Obviously_ I might not know what's behind that next monster. I don't know what the game designers have come up with: some things are _impossible_ to predict, but the basic mechanics can be learned and the more I play, the better my prediction accuracy gets. The mechanics of me learning these things is not too far away from e.g. neural network learning the same things. Again, obviously, the neural network cannot create the parts of the game world it hasn't seen. But no-one is claiming that. Nor that just by showing a little bit of game play, we could re-create the whole game. Only that it can probably learn to render the mechanics (game engine) based on user input. The authors showed some interesting results based on this.
    - zeusk 9y ago
      >> No, it isn't - just like the subject in the Chinese room experiment cannot understand Chinese yet is able to reply with perfect language to given input. > Oh, come on. This is silly semantics. That is the exact point of the thought experiment, semantics vs syntax. A computer can work well with syntax but semantics are not so easy. > You keep bringing up these useless analogues about movies and chinese boxes. Be more specific. They're useless to you because you don't seem to understand how neural network or current AI tech works. In the current fashion, generalist AI like a game engine is very much impossible. > I can look at (simple) video footage of a game I've never seen before and start to model in my head various ways how certain mechanisms can be implemented in code. I quickly learn to predict how different game elements move based on how they look and similarly looking things have moved before. Even if I didn't know anything about programming, I could learn to predict how that monster moves. In fact, playing the game trains my brain to predict these things. Sadly our computers don't have a human brain, which is entirely the point of AI. > The mechanics of me learning these things is not too far away from e.g. neural network learning the same things. Again, obviously, the neural network cannot create the parts of the game world it hasn't seen. But no-one is claiming that. Nor that just by showing a little bit of game play, we could re-create the whole game. Only that it can probably learn to render the mechanics (game engine) based on user input. The authors showed some interesting results based on this. Game states are too large to be entirely seen even by a computer. We need pruning and MCTS for simple adversarial searches in games like chess, Go; What makes you think some magical AI can imitate a game engine?

YeGoblynQueenne 9y ago

I think the acid test for a "game engine" should be the ability to use it to produce behaviour other than the original. For instance, if you somehow learn a Super Mario engine, you should be able to reprogram it to play a new level that you designed yourself. Even more importantly- you should be able to play the game and get a different outcome every time. From the paper I think the "game engine" they learn is not even capable of playing the same level in a different way than in the examples. If Mario jumps in frame X, the "game engine" will always show Mario jumping in frame X, and not know there's any other possible state at that time than frame X with Mario jumping in it. I mean, I see the value in this, totally- they've learned an automaton that can faithfully reproduce its examples and that's no mean feat. But like I say above it would make a lot more sense to define a "game engine" as an automaton that can reproduce the entire range of behaviours of its original- not just a set of its output frames and only that set of frames, in perpetuity. Of course a "game engine" in the sense I give it above is (in the case of games) a Turing complete automaton- so it would be impossible to learn to simulate it just from examples of its output (there's a lot of maths on that). So the paper settles for the easiest task. OK. But the way they present it is misleading.