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Learning Machine Learning for Game Development

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Re: Learning Machine Learning for Game Development

#11

Post by adkadskhj »

CJefferson wrote: post #3

I'm in this research area, it's facinating, but also (like many things in AI) easy to get wrong. The trickiest bit is deciding exactly what you want your AI to optimise for -- If you want a game where "anyone can win", you end up with coin toss. If you want a game where "skill is everything", you can accidentally end up with "who can click fastest". On a project I recently worked on (and I'm writing up), we ended up…

Personally, i'd like to see AI used for "simple" things, not often tied to the core gameplay loop itself. Ie conceptually if you could tie the right gameplay components to an RNG function then the user could get new/unexpected experiences for a longer time. The problem is RNG often makes crap outputs.

I'd be curious to see ML take this problem. I imagine the examples are limitless, but one that comes to mind might be procedural worlds. Rather than tuning procedural worlds around seed values, what would it look like if we try to get an ML to craft the worlds? Procedurally generated worlds often have that feeling. Things don't make sense, etc. I imagine ML could make some far more diverse patterns that are both interesting and fun.

I'd like to see this pattern of "informed RNG" in a lot of things. Skyrim with NPC patterns to feel a bit more authentic. Etc.

I'm less interested in the classic AI dreams inside games, as it seems far out and less gamey. But our current games with informed RNG? That sounds.. neat.

Re: Learning Machine Learning for Game Development

#13

Post by viktorcode »

The approach as described has one major downside. It is based on presumption that human players will employ the same strategies as a neural network which learned to play the game. It may be the case, but in reality many imbalances in the game model remain undiscovered or unused by the real players, for various reasons.

The good example of ML for playtesting is what King is doing with their Candy Crash Saga. They have trained neural network on real world usage data, from millions of players. That makes it behave like a real player too, not pathetically weak, and not inhumanely strong. This, if applicable for your game, is a better way to leverage ML.

There are other examples of neural networks finding highly unorthodox strategies in various games, when they learning to play it. It is nothing like human behaviour.

Re: Learning Machine Learning for Game Development

#15

Post by Jasper_ »

adkadskhj wrote: post #11
CJefferson wrote: post #3

I'm in this research area, it's facinating, but also (like many things in AI) easy to get wrong. The trickiest bit is deciding exactly what you want your AI to optimise for -- If you want a game where "anyone can win", you end up with coin toss. If you want a game where "skill is everything", you can accidentally end up with "who can click fastest". On a project I recently worked on (and I'm writing up), we ended up…

Personally, i'd like to see AI used for "simple" things, not often tied to the core gameplay loop itself. Ie conceptually if you could tie the right gameplay components to an RNG function then the user could get new/unexpected experiences for a longer time. The problem is RNG often makes crap outputs. I'd be curious to see ML take this problem. I imagine the examples are limitless, but one that comes to mind might be…

ML is a system that takes a large data set, and an error function, and finds a generated output that minimizes the loss. What data set and error function are you proposing for "RNG"?

Re: Learning Machine Learning for Game Development

#16

Post by Jasper_ »

Dzugaru wrote: post #6
DethNinja wrote: post #4

What if instead of spending time to train the ML model they just made a dummy client with it’s own simple probabilistic state machine or behaviour tree to balance the game? How much time and resources would be spent for above mentioned approach compared to ML approach? I think due to statistical nature of ML, it is seen as kind of an hammer to every problem that might be solved statistically(which there are lots of t…

Anecdotally: I've never seen a "simple state machine" or "simple behaviour tree" in game AI. We've recently started using deep reinforcement learning for our games and its almost like a miracle how simple, effective and scalable the system is. There are some mentioned problems like designing rewards for player enjoyment, but its definitely got a massive reduction in engineering effort.

> Anecdotally: I've never seen a "simple state machine" or "simple behaviour tree" in game AI.

That's effectively what game AIs are, today. Users want an AI they can model and simulate in their head, and isn't too brutal of a challenge. Today's machine learning cannot provide a model like that.

Re: Learning Machine Learning for Game Development

#17

Post by WORLD_ENDS_SOON »

This sort of research is maybe less flashy than say using machine learning to automatically generate game assets from photos, but I think this sort of computer-aided game design is possibly the biggest way machine learning will transform video games. As games are becoming bigger and more complicated, the problem of tuning various gameplay parameters explodes exponentially. And, this sort of tuning can have a huge effect on player retention and overall game quality.

In this research the machine learning is being used to balance the game across different asymmetric strategies (different decks in the card game), but you could imagine using similar techniques for balancing and tuning content for single player games as well. Once you have a reasonable model of the player's behavior, you can do all sorts of automatic tuning like balancing the difficulty of jumps in a platformer, tuning enemy positions in an FPS, etc.

Re: Learning Machine Learning for Game Development

#18

Post by sfjailbird »

The most obvious use of ML in this context would be to predict player actions in multiplayer games. Most such games extrapolate the actions of remote players in order to give the illusion that there is no lag between updates. I bet for many situations, a good ML implementation could make this work almost flawlessly. No idea if anyone is doing this yet.

Also, the headline is wrong: It's 'Leveraging Machine Learning for Game Development'.

Re: Learning Machine Learning for Game Development

#19

Post by matsemann »

Jasper_ wrote: post #15
adkadskhj wrote: post #11

Earlier quoted context omitted.

Personally, i'd like to see AI used for "simple" things, not often tied to the core gameplay loop itself. Ie conceptually if you could tie the right gameplay components to an RNG function then the user could get new/unexpected experiences for a longer time. The problem is RNG often makes crap outputs. I'd be curious to see ML take this problem. I imagine the examples are limitless, but one that comes to mind might be…

ML is a system that takes a large data set, and an error function, and finds a generated output that minimizes the loss. What data set and error function are you proposing for "RNG"?

That's a wrong simplification of ML. Take RL for instance.

And the parent already explained a concept: generating maps. It could still have an RNG as the base (noise function over something), but then use ML to place elements based on existing human-made maps.

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