Supporting game design with evolutionary algorithms

https://www.gamedeveloper.com/design/supporting-game-design-with-evolutionary-algorithms

by kevthecoder • 2 years ago

75 57 2 years ago

jncfhnb9• 2 years ago

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.

dartos2• 2 years ago

Yeah. A truly balanced game is boring. There’s no drama or tension.

You need the power fantasy of being strongest characters and the underdog tale of winning against the strongest characters as a less meta one.

Log_out_• 2 years ago

Why not abandon the idea of balance entirely and tailor balance situational for maximum drama.make the meta shakespearean..

10000truths2• 2 years ago

This can be accounted for by training bots using player inputs from historical games, no? If you then partition the training set by player ranking, you'd presumably get a set of bots that approximate a player from each ranking. With that, you could simulate the effect of a balance change on players of various skill levels.

jncfhnb9• 2 years ago

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 super over powered, and your optimization algorithm sets the AK damage to 0; what are your “human” bots going to do? Because all the training data says to use the AK.

This approach only makes sense if you’re evaluating bot-optimal play outcomes.

It also takes away a lot of the design thinking behind balance. You probably don’t want to nerf the AK. You probably want to buff counterplay options (guns are not a great example but still)

bitshiftfaced• 2 years ago

Suppose you wanted to rebalance the rules of chess so that the black pieces had equal odds. You really only need to achieve this at the superhuman skill level, and then you could let the matchmaking algorithm could do the rest of the work.

PicassoCTs• 2 years ago

I found that to design games for the crowd seperatly and merge them, works wonders. One crowd gets a hyper-challenging rts, the others get to play heroe units making all the difference in a small dota game, playing off the the hyper-rts. The OPs can take over and micro, but they wont never micro as well as a human, for whom it is the whole game.

One example.. of course, you goto keep it civil. No pro players dunking on the mom&pop gamers who just want to have 5mins of fun on a tablet.

SpaceManNabs2• 2 years ago

I agree that a rudimentary evolutionary algorithm probably wouldn't work since they often don't take into account multimodality. There are global stochastic algorithms that take into account this sort of multi modality. Most particle swarm framworks could handle this sort of bipartite nature with a more complicated fitness function. Differential Evolution frameworks in R also allow you to do a post operation on each candidate, which include something like rejecting it because it does not seem to lie in one of the two modes.

pfdietz4• 2 years ago

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

jncfhnb9• 2 years ago

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

aschearer• 2 years ago

Programmers always want to automate content creation. I get it as I’m a coder and it’s a fun problem space. Plus creating content is super time and skill intensive. It’s really hard to get right in my experience. Kind of like trying to automate writing a novel…

OK I read the article. I'm very skeptical of this approach. I doubt we can actually uncover fitness functions that reliably maps to "fun", and I believe it would require huge engineering effort to keep the game "simulable." Their examples aren't convincing. What would be convincing is a full, complex, and _fun_ game using these techniques.

Also the article seems like an ad for their AI solution.

delichon• 2 years ago

Programmers always want to automate content creation

We have a god complex that brain surgeons can only envy. How many universes have they built?

programd3• 2 years ago

I suspect everyone on HN started out wanting to build COOL WORLDS as a kid!

cubefox5• 2 years ago

Kind of like trying to automate writing a novel…

So you are saying procedural level design could be solved with generative AI?

itishappy5• 2 years ago

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

SpaceManNabs2• 2 years ago

I doubt we can actually uncover fitness functions that reliably maps to "fun"

You don't have to. Get empirical data and form a proxy evaluator. Usable enough for most evolutionary algorithms. I've done this sort of stuff for very subjective metrics and actually sold something with it.

programd3• 2 years ago

The whitepaper on the Grail framework is interesting [1]. It's an AI C++/C# framework you can use in game development.

I would call it a very good implementation of "old school" AI, where the behavior of your actors is all about utility curves, Monte Carlo search, and genetic algorithms. Basically all math/algorithm based stuff, kind of like old expert system AI implementations.

Of course "new school" AI is all about neural networks that can automatically learn those complex actor behaviors without the developer explicitly specifying all those mathematical algorithms.

Like many people here I'm very interested in working on hooking up modern "new school" AIs to virtual worlds, so it's very interesting to see Grail as a good concrete example of the algorithmic approach to game AI. I suspect some hybrid fusion of both approaches may give us some interesting and fun AI behavior.

[1] https://grail.com.pl/media/Grail_Whitepaper_June_2021.pdf

uoaei• 2 years ago

The dichotomy of interest isn't really NNs vs GAs but about stochasticity vs determinism. The user/player doesn't care which it is until they learn to see the patterns and to reliably exploit the rules. Once you notice these patterns, the spell is partially broken and immersion is compromised. That's part of the reason stochastic methods have been such a hit throughout the history of game development for NPC behavior.

bob1029• 2 years ago

I think things get really interesting when you combine small, efficient ANNs with evolutionary/genetic algorithms.

The biggest challenge with these is coming up with a function that clearly represents the fitness of a candidate. EAs are worthless if you can't provide good selection pressure over time. There are always multiple conflicting objectives in the most practical cases. I've recently discovered that you can skip all the nasty objective weighting business if you just select the Pareto front every generation. You never know when a trip down a less important path might result in the most optimal global solution.

shortrounddev2• 2 years ago

I think Quake 3 used a bit of genetic algorithms to optimize some of the variables of the bots

Rhapso2• 2 years ago

Missing the OG: NERO https://nn.cs.utexas.edu/nero/about.php

NEAT is really well suited to modeling behaviors in a less structured way and gets used to play videogames.

NotGMan• 2 years ago

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.

Rhapso2• 2 years ago

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

adastra22• 2 years ago

There is no good metric for fun.

pfdietz4• 2 years ago

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.

Supporting game design with evolutionary algorithms

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