Sadly, it seems the game industry cannot permit themselves to actually learn from academic research as a means of advancing the industry.
Imagine what a game that could get this right would be.
(Well, other than stupidly successful and recession proof.)
Out of interest, and to perhaps help the discussion going. In what way are you thinking of a machine learning implementation in game development as advancement of the industry?
Apologies, in my experience, there is no such thing as genuine interest on these forums.
But… since I can be wrong without knowing it (grin), I will clarify by adding questions to contemplate and suggested topics to read on if you’re interested:
What if procedural generation could make a game from scratch using the taxonomies and assets you craft?
What if AGI NPCs replaced the hard-coded NPC?
What if AGI/ML Mobs learned how to fight players over time?
What if AGI/ML worlds were the missing ingredient in standing up a multiverse?
None of these questions will be unanswered by the year 2050. Most will have been answered by 2025.
Just wilding on my favorite geekery and how sad I am to see that, yet again, the companies we’d most expect to be on the forefront are, well, not.
#machinelearning #artificalgeneralintelligence #ProceduralEverything #HelloWorld
Good post, but never delve into r/Iamverysmart, as a word of caution my friend.
Been there, done that, have the mousepad, keychain, t-shirt, visor, AND koozie. 
Owning the joke.
I respect that. Have a good day.
There may not be much, but there certainly is some left 
.
These are things which are being worked on, also various research papers exist on this subject.
To stick to Two Minute Papers, of interest are these two videos for example:
The first using Machine Learning recently to create CS:GO bots. The second to have OpenAI defeat the DOTA2 world champions in 2019.
And there is plenty of similar research being done in this field of computer science.
There is however a serious concern in this matter. Machine learning will ultimately, inevitably, reach a state of competency in which it will be able to defeat the best human players in the world. That’s just the machine doing what its designed to do, learn. As such, the difficulty in this is, how do you counter this.
The same machine learned AI opposing me in DOTA 2 as the AI that won from the world champions would completely obliterate me. Simply put, it would not make for a better game. It would be far too hard. So rather, what you’d want is a much more tailor made experience. Seeing as we all know, too easy isn’t fun. But too hard isn’t fun either. Nobody likes that experience of wanting to smash your keyboard and throw your monitor out of the window because you died for the 10th time in 5 minutes.
However, that process of teaching an AI on a personal level, is also a tedious one.
This makes it hard to create a workable solution. The option which you have is to train various levels of AI using machine learning, at different scales of players, to then turn off the machine learning afterwards and keep the AI functioning on the obtained level. This of course is possible. However, such training sessions for different levels of player skill do come with issues of their own. You cannot do this post launch, this would need to be done prior to launch. But how does one determine level of player skill prior to launch? Competitive play competitions to determine skill level? This may work in competitive multiplayer games, but how do you do this for example in a single player RPG? This is challenging.
Procedural generation for example is used, extensively, within the underlying engine for Microsoft Flight Simulator. In which the entire world is recreated, using terrain data and satellite data for one, procedurally filled in using machine learning secondly. Some parts of the world are hand touched. But as you can imagine, recreating the entire world with the level of graphical fidelity as provided by Flight Simulator, is simply not possible without such procedural generation.
However, this comes with issues too. These sort of endless worlds, and Earth is even finite still, are large, absolutely massive. We’re talking petabytes of data for Flight Simulator. Which we stream via Microsoft’s Azure cloud in this case. But the question is of course, how practical is this for all games.
And for smaller scale games, let’s be fair. The procedurally generated world in for example Flight Simulator is impressive from within the sky. But on a ground level, a city like New York in that title does not compare to for example a handcrafted city in GTA 5. Procedural generation just isn’t that far yet. Will it come over time? No doubt. But it’s still in its infancy.
Machine learning within game development will at first most likely just remain supportive in nature, as within many other fields. Think not so much procedurally generating an entire world, but filling a world segment to be retouched or for example code prediction to ease the programming side of things by predicting code patterns.
Players don’t want to fight real AI mobs as the players would always lose. This is a game where players want to feel powerful, they can’t do that against an AI that practices every permutation until it finds the most efficient path/result and can do this 1,000,000 times faster than a human player.
You’d then have to invent ways to dumb down the AI just so you could get back to a spot like we have today.
I’m quite familiar with the penchant of business to succeed just enough to fail when it comes to requisite investment in R&D and infrastructure.
This in no way occludes that the technology has been around long enough to be considered ‘maturing’, better than development practices in the gaming industry, clearly, in which it seems designers and publishers alike largely content themselves with scavenging and recycling game designs that (might) pay the bills, but nearly to a one, cannot sustain nor maintain more than competitive mediocrity.
It’s always this way right before something really different shows up.
My point was all of this competency has been “out there” and getting cheaper and more reliable by the minute, and here we are, moping about on a forum for a product and company who, from all appearances, would rather just be another 3 year blip on an otherwise silt-settled segment.
TL;DR - No one wants innovation until they’re losing money because of it. Everyone wants to learn from the gamblers that comprise the early adopters. (Looking at you, Koster.)
edit to clarify some wording.
Did you just finish watching Free Guy and had this new great idea?
Cute. But no. I’m mostly just laying up easy shots to demonstrate how bizarre it is to watch an industry reliant upon technology consistently fail at designing it, mostly due to a perverse conviction (wrong) that they innately can make ‘deep tech’ better than the technology industry.
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