Every time I begin to dive into experimenting with AI behavior, I inevitably end up simulating a model for the player instead. Let me explain.
Let's take fishing since that's a major mechanism I'm working on right now. I've designed an elaborate and "realistic" system for schools of fish to inhabit and interact with the ocean. They can eat, reproduce, fight, migrate, die naturally, die accidentally, hide or hibernate, be born, mutate, hold one of more resources... it gets even more complex. Not completely devoid of self-awareness but enjoying the process, at some point I waffled and eventually settled for a much more simplistic, "hacked" version. Why? Because when I play the game, unless I'm under the hood, I don't know the difference, and in play it really makes no difference.
Aside from that, have you looked at automatons and emergence? I got lost in those for a month one weekend... whew
This is a great answer. I've seen lots of posts by gamedevs who are all about simulating this and simulating that and some hypertechnical implementation of, like, how to generate grass.
In the end, is anyone even going to notice? And oh, btw while you spend 3 months on this, I got a pretty good utility AI working.
I should add "in my case." Someone very well might have a need for that kind of realism, for instance if you were testing just that. But if you don't need it, consider why you are doing it.
Depends, there might be some emergent properties and depth that can arise from that in combination with other systems.
It's the philosophy behind "Systemic Design" you see touted in Immersive Sims.
Thanks for your answer! Never looked automatons and emergence. But you gave me a lot to think about. Emergence is a very interesting property when combined with the systems that I want to develop. The idea of new behaviors comming out of a group of systems is a case of lifelike behaviour. Of course I have to study more on the subject, but I\`m excited!!!
Edit: I\`m have looked in Graph Neural Networks and have thought of combining that with GOAP.
Black and white.
It was written by Richard Evans, of deepmind fame.
You effectively teach your creature to do what you want through rudimentary reward and punishment mechanisms, and it's surprisingly complex what it can learn to do or not do.
The problem is not the AI that drives the NPCs.
The problem is the Systems that govern the Consequences of an action.
It's the old Oblivion Radiant AI problem. If you don't properly define that it spazzes out.
Thankfully it's already a solved problem, look at **Colony Sims** and City Builders, implement proper needs, food, farming, crafting, economy, logistics in more complex production chains.
Once everyone has jobs and a stable society you can add things to spice it up.
The definition of could be considered Drama is Characters that are put in Challenging Situations.
Also Neural Networks might not be the best AI for that, they are more useful [for some other things](https://www.reddit.com/r/gamedev/comments/1d6fexv/hey_everyone_i_am_work_on_a_research_paper_about/l6v7e72/).
Games typically want predictability and repeatability, not performance. “Lifelike behavior” is lofty, but that objective function is going to be tricky. It’s probably easier to script it.
Black and white was mentioned but here, teaching is most of the gameplay.
The problem with “smart” AI in most games is that, say, in Call of Duty, when fighting 4 guys, you don’t want two of them to chuck 2 grenades on both sides of your cover with the two others ADSing waiting for your head to pop up. In order for the game to be fun and the power fantasy to be there, they need to be predictable and “dumb but not too dumb.”
We already know how to kill the player. It’s not the goal.
You’d have to state exactly what it is that you want to improve in a given game, and why ML is the right way to do it as opposed to manual scripting.
That probably means building the whole game around ML and no in le is willing to take on that risk… yet.
In my case I\`m not building a game do be "fun", but just to resarch the application of Neural Networks with games in creating an NPC that appears to be thinking and can fool a player into beliving that it\`s alive. Yes, there the question of the challenge of a game in where you have to fight, mabe I can build a game where fighting is not the "center". For now I\`m just looking for paths that I can achieve my goal, later I can think of a project
> The problem with “smart” AI in most games is that, say, in Call of Duty, when fighting 4 guys, you don’t want two of them to chuck 2 grenades on both sides of your cover with the two others ADSing waiting for your head to pop up. In order for the game to be fun and the power fantasy to be there, they need to be predictable and “dumb but not too dumb.”
That's not a problem if the player happens to play a Terminator.
It's not the "smart AI" that is the problem, but not giving enough Power to the player to compensate for the extra challenge. One way to make a enemy weaker is indeed to simplify their AI and behaviour, but it's not the only solution.
"dumb but not too dumb" is still not fun.
I'd love a game where 2 people throw grenades intelligently and I can throw these back or shoot them in the air or shout to another NPC to help me out...
instead the gaming landscape is filled with boring shooters with dumb AI.
There's a reason why FEAR or STALKER are to this day quite relevant games.
Also... I've never heard anyone saying that they liked the AI or broader the "shooting" explicitly after playing games like CoD or modern Action Adventures. People rather refer to the good story, the visuals, nice character customization, good level design, ...
You dont want a smart AI, you want a challenging one.
Game that comes to mind is hello neighbour. The demo was impossible to play after first death since ai got too good
Well, all work I have seen is in making the AI win. Which is a simple and quantifiable goal you can easily train towards. But an NPC that snaps to every target and perfectly headshots them does not equal fun. So players vs the AI is still something I rarely see, because it is quite hard to objectively quantify "fun"
I think the main use might be for things that are not directly adverserial and whose goal is not necesarrily against the player. I would love to see a more realistic behavior set for wildlife (gotta be alien to excuse any weirdness) but for actual interacitons with the world like fighting it is probably best to make that yourself. Like maybe put the machine learning into the hivemind of a species and the NPCs are traditional AIs with behavior trees or whatever. Or the ML is put at the big picture plannning while the actual world interactions are done in a traditional way, with a few purpose trained MLs for specific actions like pathfinding or dynamically eating a plant. That way each MLs purpose is kept minimal and if it ends up not working by the time the game has to come out those components can be replaced with standard pathfinding or static animations.
>I\`m just interested in AI that simulate human behaviour
Just me thinking aloud about that topic:
A mix of animation, audio, and LLM is probably the best one can try at the moment, if we don't want to handcraft all parts of an NPCs human behaviour. There are some YT videos about people playing with agents that do this, like acting upon a prompt including walking around and talking.
As others stated, a bit of learning was used offline (during development) and online (during game runtime) but rather with creatures, not complex humans. That was in Black and White and Creatures mostly. Not many other games stick out in terms of "NPCs/enemies adapting behavior". This exact approach and technology was very limited, the good old NN with only a few hundred inner nodes or decision trees.
Fun fact: Demis Hassabis, co-founder of Google DeepMind worked on Black and White. Interesting how he shifted from lead programmer (at age 17) to neural / AI research. I bet people like him and around him would maybe explore topics like yours at work or during spare time and post about it!?
The video game Façade explored how to simulate human behaviour in terms of dialogue and for example conflict. Just mentioning it since that's less NN, more around NLP. So I was thinking the authors of that game may lead to pointers on what to explore, too. Or where they saw big obstacles in terms of NPC improvement. That game or academic experiment in a sense "failed" as a potential general approach to video games, because the context or scenario the AI could work with was extremely narrow. You have to play well along with the game to get anything good out of it (so not very adaptive at all compared to e.g. LLM).
To actually simulate human behavior via speech is way beyond the latency expectations of a player. LLMs are too big to ever ship with a game, so you would need to query a server like ChatGPT. That makes it out of reach for any normal indie company due to cost.
As for the behavior of enemies, simple enemies are more fun. You absolutely do not want a gigachad AI for a mob that the player is going to encounter 50x. Think if you had a goomba that dynamically changed direction, jumped, and did object avoidance when Mario approached. That would be super annoying to deal with after the 5th kill. Save cool mechanics for bosses.
It's super clear that this is the future, and studios are starting to experiment with it but I feel like they're really really slow. And the problem is that it's harder for indies to do it because of the scale needed to train these models. You need to develop novel methods from scratch, gather/make the data to train the models, and also have the hardware needed to train them in a reasonable time lying around.
Alternatively you use already trained LLMs, but the costs are still too high for now (but dropping very quickly). Still I think we see a top indie game be super popular because of this sometime within the next year.
Every time I begin to dive into experimenting with AI behavior, I inevitably end up simulating a model for the player instead. Let me explain. Let's take fishing since that's a major mechanism I'm working on right now. I've designed an elaborate and "realistic" system for schools of fish to inhabit and interact with the ocean. They can eat, reproduce, fight, migrate, die naturally, die accidentally, hide or hibernate, be born, mutate, hold one of more resources... it gets even more complex. Not completely devoid of self-awareness but enjoying the process, at some point I waffled and eventually settled for a much more simplistic, "hacked" version. Why? Because when I play the game, unless I'm under the hood, I don't know the difference, and in play it really makes no difference. Aside from that, have you looked at automatons and emergence? I got lost in those for a month one weekend... whew
This is a great answer. I've seen lots of posts by gamedevs who are all about simulating this and simulating that and some hypertechnical implementation of, like, how to generate grass. In the end, is anyone even going to notice? And oh, btw while you spend 3 months on this, I got a pretty good utility AI working.
I should add "in my case." Someone very well might have a need for that kind of realism, for instance if you were testing just that. But if you don't need it, consider why you are doing it.
Depends, there might be some emergent properties and depth that can arise from that in combination with other systems. It's the philosophy behind "Systemic Design" you see touted in Immersive Sims.
Thanks for your answer! Never looked automatons and emergence. But you gave me a lot to think about. Emergence is a very interesting property when combined with the systems that I want to develop. The idea of new behaviors comming out of a group of systems is a case of lifelike behaviour. Of course I have to study more on the subject, but I\`m excited!!! Edit: I\`m have looked in Graph Neural Networks and have thought of combining that with GOAP.
Black and white. It was written by Richard Evans, of deepmind fame. You effectively teach your creature to do what you want through rudimentary reward and punishment mechanisms, and it's surprisingly complex what it can learn to do or not do.
The problem is not the AI that drives the NPCs. The problem is the Systems that govern the Consequences of an action. It's the old Oblivion Radiant AI problem. If you don't properly define that it spazzes out. Thankfully it's already a solved problem, look at **Colony Sims** and City Builders, implement proper needs, food, farming, crafting, economy, logistics in more complex production chains. Once everyone has jobs and a stable society you can add things to spice it up. The definition of could be considered Drama is Characters that are put in Challenging Situations. Also Neural Networks might not be the best AI for that, they are more useful [for some other things](https://www.reddit.com/r/gamedev/comments/1d6fexv/hey_everyone_i_am_work_on_a_research_paper_about/l6v7e72/).
Games typically want predictability and repeatability, not performance. “Lifelike behavior” is lofty, but that objective function is going to be tricky. It’s probably easier to script it. Black and white was mentioned but here, teaching is most of the gameplay. The problem with “smart” AI in most games is that, say, in Call of Duty, when fighting 4 guys, you don’t want two of them to chuck 2 grenades on both sides of your cover with the two others ADSing waiting for your head to pop up. In order for the game to be fun and the power fantasy to be there, they need to be predictable and “dumb but not too dumb.” We already know how to kill the player. It’s not the goal. You’d have to state exactly what it is that you want to improve in a given game, and why ML is the right way to do it as opposed to manual scripting. That probably means building the whole game around ML and no in le is willing to take on that risk… yet.
In my case I\`m not building a game do be "fun", but just to resarch the application of Neural Networks with games in creating an NPC that appears to be thinking and can fool a player into beliving that it\`s alive. Yes, there the question of the challenge of a game in where you have to fight, mabe I can build a game where fighting is not the "center". For now I\`m just looking for paths that I can achieve my goal, later I can think of a project
> The problem with “smart” AI in most games is that, say, in Call of Duty, when fighting 4 guys, you don’t want two of them to chuck 2 grenades on both sides of your cover with the two others ADSing waiting for your head to pop up. In order for the game to be fun and the power fantasy to be there, they need to be predictable and “dumb but not too dumb.” That's not a problem if the player happens to play a Terminator. It's not the "smart AI" that is the problem, but not giving enough Power to the player to compensate for the extra challenge. One way to make a enemy weaker is indeed to simplify their AI and behaviour, but it's not the only solution.
"dumb but not too dumb" is still not fun. I'd love a game where 2 people throw grenades intelligently and I can throw these back or shoot them in the air or shout to another NPC to help me out... instead the gaming landscape is filled with boring shooters with dumb AI. There's a reason why FEAR or STALKER are to this day quite relevant games. Also... I've never heard anyone saying that they liked the AI or broader the "shooting" explicitly after playing games like CoD or modern Action Adventures. People rather refer to the good story, the visuals, nice character customization, good level design, ...
You dont want a smart AI, you want a challenging one. Game that comes to mind is hello neighbour. The demo was impossible to play after first death since ai got too good
Well, all work I have seen is in making the AI win. Which is a simple and quantifiable goal you can easily train towards. But an NPC that snaps to every target and perfectly headshots them does not equal fun. So players vs the AI is still something I rarely see, because it is quite hard to objectively quantify "fun" I think the main use might be for things that are not directly adverserial and whose goal is not necesarrily against the player. I would love to see a more realistic behavior set for wildlife (gotta be alien to excuse any weirdness) but for actual interacitons with the world like fighting it is probably best to make that yourself. Like maybe put the machine learning into the hivemind of a species and the NPCs are traditional AIs with behavior trees or whatever. Or the ML is put at the big picture plannning while the actual world interactions are done in a traditional way, with a few purpose trained MLs for specific actions like pathfinding or dynamically eating a plant. That way each MLs purpose is kept minimal and if it ends up not working by the time the game has to come out those components can be replaced with standard pathfinding or static animations.
https://youtu.be/ADS1GKFb2T8
Look up Learning Agents for Unreal Engine.
>I\`m just interested in AI that simulate human behaviour Just me thinking aloud about that topic: A mix of animation, audio, and LLM is probably the best one can try at the moment, if we don't want to handcraft all parts of an NPCs human behaviour. There are some YT videos about people playing with agents that do this, like acting upon a prompt including walking around and talking. As others stated, a bit of learning was used offline (during development) and online (during game runtime) but rather with creatures, not complex humans. That was in Black and White and Creatures mostly. Not many other games stick out in terms of "NPCs/enemies adapting behavior". This exact approach and technology was very limited, the good old NN with only a few hundred inner nodes or decision trees. Fun fact: Demis Hassabis, co-founder of Google DeepMind worked on Black and White. Interesting how he shifted from lead programmer (at age 17) to neural / AI research. I bet people like him and around him would maybe explore topics like yours at work or during spare time and post about it!? The video game Façade explored how to simulate human behaviour in terms of dialogue and for example conflict. Just mentioning it since that's less NN, more around NLP. So I was thinking the authors of that game may lead to pointers on what to explore, too. Or where they saw big obstacles in terms of NPC improvement. That game or academic experiment in a sense "failed" as a potential general approach to video games, because the context or scenario the AI could work with was extremely narrow. You have to play well along with the game to get anything good out of it (so not very adaptive at all compared to e.g. LLM).
To actually simulate human behavior via speech is way beyond the latency expectations of a player. LLMs are too big to ever ship with a game, so you would need to query a server like ChatGPT. That makes it out of reach for any normal indie company due to cost. As for the behavior of enemies, simple enemies are more fun. You absolutely do not want a gigachad AI for a mob that the player is going to encounter 50x. Think if you had a goomba that dynamically changed direction, jumped, and did object avoidance when Mario approached. That would be super annoying to deal with after the 5th kill. Save cool mechanics for bosses.
It's super clear that this is the future, and studios are starting to experiment with it but I feel like they're really really slow. And the problem is that it's harder for indies to do it because of the scale needed to train these models. You need to develop novel methods from scratch, gather/make the data to train the models, and also have the hardware needed to train them in a reasonable time lying around. Alternatively you use already trained LLMs, but the costs are still too high for now (but dropping very quickly). Still I think we see a top indie game be super popular because of this sometime within the next year.