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Supporting game design with evolutionary algorithms

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Re: Supporting game design with evolutionary algorithms

#41

Post by jncfhnb »

bongodongobob wrote: post #37
jncfhnb wrote: post #32

Earlier quoted context omitted.

That’s not what “prove” means and notably is not specific to black which was the more significant claim. I think it is highly likely that perfect play results in a tie for both sides.

That's what a draw is my guy.

Yes I’m agreeing with you. But I’m saying it’s not proven and it’s not expected to be favoring either side as claimed.

Saying black can at best tie against perfect play is not the same thing as saying both players can at best tie when facing perfect play.

Re: Supporting game design with evolutionary algorithms

#42

Post by jncfhnb »

10000truths wrote: post #35
jncfhnb wrote: post #10

Earlier quoted context omitted.

Not really. It would take immense effort to train bots to play “like humans” and not “as performantly” as humans which is very different. And if you’re going to be optimizing game parameters that means you’re assuming that either the AI doesn’t change its behaviors even though the game is different or you’re assuming that humans will adapts in the same way the bots do. Like if all the humans use the AK because it’s s…

> Not really. It would take immense effort to train bots to play “like humans” and not “as performantly” as humans which is very different. There is precedent in Maia Chess, which does a good job of mimicking human chess players at various ELO ratings. Of course, it's a lot more difficult to extrapolate to games with significantly more state/movesets, but I imagine that this space will be further explored in the near…

You’ve largely missed or ignored my point. If you change the game your AI either fails to adapt or adapts in an AI way. But it can’t reliably adapt in a human way without data on how humans adapt to the change. That’s just not how it works. Maia won’t mimic humans hypothetical behavior if you make it so that bishops can also move like knights.

There’s nothing wrong with using bots in play test data but you shouldn’t ever expect an optimization algorithm to generate fun balanced game mechanic stats based on their behaviors by tweaking things until the bots are evenly matched.

Re: Supporting game design with evolutionary algorithms

#43

Post by pfdietz »

Oh wow, this kind of idea goes way back, to Doug Lenat's Eurisko. Good old fashioned AI. He famously used that to craft winning strategies for Traveller Trillion Credit Squadron, a non-computer game.

Today's game designers might use this sort of thing to find game-breaking exploits before those damned players do.

Re: Supporting game design with evolutionary algorithms

#44

Post by pfdietz »

jncfhnb wrote: post #2

Seems unlikely to work imo. Games need to account for the fact that 90% of the player base will be deeply unskilled and the remaining 10% will be tightly invested in a meta of dominant strategies. You’d need the bots to be able to consider this and also not just equalize every character.

You can get around that by telling those 10% to f- off and play something else.

Re: Supporting game design with evolutionary algorithms

#45

Post by jncfhnb »

pfdietz wrote: post #44
jncfhnb wrote: post #2

Seems unlikely to work imo. Games need to account for the fact that 90% of the player base will be deeply unskilled and the remaining 10% will be tightly invested in a meta of dominant strategies. You’d need the bots to be able to consider this and also not just equalize every character.

You can get around that by telling those 10% to f- off and play something else.

The ones who are most interested in your game to actually learn it? That’s a strat but it’s certainly not the game dev meta

Re: Supporting game design with evolutionary algorithms

#46

Post by cubefox »

DennisP wrote: post #25
itishappy wrote: post #18

Earlier quoted context omitted.

In theory. It'll be more convincing once generative AI proves itself useful for writing novels.

AI can already write short stories, just not very good ones. It'll be doing novels once the context windows are big enough.

Note also that base models (foundation models) are much better at writing fiction than models tuned for chat. See https://nostalgebraist.tumblr.com/post/706441900479152128/no...

Re: Supporting game design with evolutionary algorithms

#47

Post by pfdietz »

jncfhnb wrote: post #45
pfdietz wrote: post #44

Earlier quoted context omitted.

You can get around that by telling those 10% to f- off and play something else.

The ones who are most interested in your game to actually learn it? That’s a strat but it’s certainly not the game dev meta

Amazingly, "better player" doesn't imply "better customer". That is, unless it's a P2W game, where better = willing to spend more money.

Re: Supporting game design with evolutionary algorithms

#48

Post by Rhapso »

NotGMan wrote: post #38

Optimal gameplay balance != fun gameplay. Many games were ruined post-release because developers tried to make the gameplay "more balanced". This usually leads to everything feeling the same. Helldivers 2 was a good recent example, which is suprising since it's not a PvP game at all but a co-op PvE only.

well, extract a good metric for "fun" (like engagement) and make that the optimization criteria. It would be equally as easy.

Re: Supporting game design with evolutionary algorithms

#49

Post by [deleted] »

jncfhnb wrote: post #2

Seems unlikely to work imo. Games need to account for the fact that 90% of the player base will be deeply unskilled and the remaining 10% will be tightly invested in a meta of dominant strategies. You’d need the bots to be able to consider this and also not just equalize every character.

[deleted]

Re: Supporting game design with evolutionary algorithms

#50

Post by adastra22 »

Rhapso wrote: post #48
NotGMan wrote: post #38

Optimal gameplay balance != fun gameplay. Many games were ruined post-release because developers tried to make the gameplay "more balanced". This usually leads to everything feeling the same. Helldivers 2 was a good recent example, which is suprising since it's not a PvP game at all but a co-op PvE only.

well, extract a good metric for "fun" (like engagement) and make that the optimization criteria. It would be equally as easy.

There is no good metric for fun.
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