Playing complex strategy games for many years, one of the things that irks me the most is that hard AI levels often just give the dumb AI cheats to simulate it being smarter. To me, it’s not very satisfying to go against cheating AI. Are any games today leveraging neural networks to supplant or augment hand-written decision tree based AI? Are any under development? I know AI can be resource intensive, but it seems that at least turn based games could employ it.
I don’t know what it’s using specifically under the hood, but in Street Fighter 6 Capcom recently added a new AI opponent you can fight that they say is trained on actual player ranked matches and fights more like a human opponent. You can even have it try to mimic your own playstyle if you’ve played enough.
It can do some odd things and its mimicry isn’t perfect. But it definitely doesn’t feel like the typical high difficulty CPU opponent which uses things like input reading to react faster than a real player ever could.
…it also has been seen teabagging.
You can train it in mirror matches, but the V Rivals that you can fight other than your own mirror are an amalgamation of a particular rank. There’s a whole lot of skill variance in Master rank alone, so it might be good for training me against Dhalsim, because hardly anyone plays Dhalsim, so no one knows the matchup, but it won’t help me learn how to beat Punk, specifically.
Chess.
For most games, it’s not difficult to make AI that can absolutely destroy humans. But it turns out to be very difficult to make AI that feels like a fun and engaging challenge to a human. Hardest of all is making AI that realistically plays like a human does.
The advantage of a neural AI, in my mind, isn’t that it is better. It is that it is worse in a way that is fun.
Chess/Go? AlphaZero would fit that description. Also think they were tackling StarCraft as well?
Oh that’s really interesting; I hadn’t considered racing games as a genre to benefit from this type of machine learning. I guess I figured there’s not so much to AI there that it’s necessary, at least when we already know the “ideal lap line” for cars to follow, but yeah it gets a lot harder when considering other drivers on the track and a huge array of unique car models with their own handling and performance characteristics.
The challenge is that AI for a video game (even one fixed game) is very problem specific and there’s no generalized approach/kit for developing AI for games. So while there’s research showing AI can play games, it’s involved lots of iteration and AI expertise. Thats obviously a large barrier for any video game and that doesn’t even touch the compute requirements.
There’s also the problem of making AI players fun. Too easy and they’re boring, too hard and they’re frustrating. Expert level AI can perform at expert level, which wouldn’t be fun for the average player. Striking the right difficulty balance isn’t easy or obvious.
I wouldn’t mind an AI using unorthodox strategies, but yeah that’s a good point that fine tuning it to be fun is a big challenge. Speaking of “non-player-like behavior”, I wonder if AI could be used to find multiplayer exploits sooner, though the problem there is you don’t really have much training data besides QA and playtesters before a full release.
Historically, AI has found and used exploits. Before OpenAI was known for chatgpt, they did a lot of work in reinforcement learning (often deployed in game-like scenarios). One of the more mainstream training strategies (pioneered at OpenAI) played sonic and would exploit bugs in the game, for example.
The compute used for these strategies are pretty high though. Even crafting a diamond in Minecraft can require playing for hundreds of millions of steps, and even then, AI might not constantly reach their goal. Theres still interesting work in the space, but sadly LLMs have sucked up a lot of the R&D resources.
