5 ms·

All the builds are generated from scratch. It's maths descriptions of the items/abilities + the lol formula. So the vast majority of the builds are novel. (Some

All the builds are generated from scratch. It's maths descriptions of the items/abilities + the lol formula. So the vast majority of the builds are novel. (Some of them happen to line up with what is currently played.) Are they effective is a good question, basically that needs lots of play testing by real players. I've added guides so people can tell other people about good builds they have found. So when developing it, I took some existing builds from the internet and I broke them down into a ratio of stats aka (1 damage: 1.5 cooldown (now AH): 1.2 late game health) and then had the GA reconstruct those builds from the ratios. On the builds being played by the pros created by professional analysts, I got back very similar looking builds. On random builds from site like mobafire I got back completely different builds. Thinking back, when Cloak of Agility was briefly buffed last year, it came back with the exact amount of them to spam and exactly when to do it. It took 10 minutes and the player base took 3 weeks to work it out.

tylerhou 6y ago
One big unmodelled parameter is the enemy team composition. For example, against teams with easy-to-land poke, QSS is better than Banshee’s Veil. If you modeled team composition, it may be possible to even model pick-ban phase via min-max search. A full exhaustive search would be computationally impossible, but you could approximate it AlphaGo style with a neural network estimator + Monte Carlo tree search. You may be able to sell such software to professional teams.
- ReflectedImage 6y ago
  So in the work the PhD is built upon, you focus entirely on what you can do, ignoring the enemy side. It might not be correct but it's a strategy you can take. You can still sort of address it by saying you need to have the ability to deal with X. Pick-ban phase optimization already exists at least for Dota 2.

tylerhou 6y ago

One big unmodelled parameter is the enemy team composition. For example, against teams with easy-to-land poke, QSS is better than Banshee’s Veil. If you modeled team composition, it may be possible to even model pick-ban phase via min-max search. A full exhaustive search would be computationally impossible, but you could approximate it AlphaGo style with a neural network estimator + Monte Carlo tree search. You may be able to sell such software to professional teams.

ReflectedImage 6y ago
So in the work the PhD is built upon, you focus entirely on what you can do, ignoring the enemy side. It might not be correct but it's a strategy you can take. You can still sort of address it by saying you need to have the ability to deal with X. Pick-ban phase optimization already exists at least for Dota 2.