- Joined
- Feb 10, 2017
- Messages
- 2
- Thread Author
- #1
I'm curious to know if anyone knows of any research into using machine learning to improve botting. In this age of human jobs being taken by machines, why not have them play our games too?
Very broadly I see two major applications. The first is obviously using machine learning to play the game itself, or supplement hard coded design. It could be useful for complex pattern recognition, among other things.
The second big application I see is using it to audit the "detectability" of bots. The other team is already using machine learning to detect cheating, so why not take a page from their book? If we're able to very accurately differentiate between real players and our own bots, it could allow much better research into anti-ban.
It's also worth noting that these two strategies could be combined into a scheme of what's called "adversarial" machine learning. This would entail training the bot program to create hard to detect programs, while simultaneously training a ban program to differentiate between bots created by the other program and real players.
Let me know what y'all think.
Very broadly I see two major applications. The first is obviously using machine learning to play the game itself, or supplement hard coded design. It could be useful for complex pattern recognition, among other things.
The second big application I see is using it to audit the "detectability" of bots. The other team is already using machine learning to detect cheating, so why not take a page from their book? If we're able to very accurately differentiate between real players and our own bots, it could allow much better research into anti-ban.
It's also worth noting that these two strategies could be combined into a scheme of what's called "adversarial" machine learning. This would entail training the bot program to create hard to detect programs, while simultaneously training a ban program to differentiate between bots created by the other program and real players.
Let me know what y'all think.