Hi, I'm a programmer and I've been playing with artificial learning a lot recently, and I'm really impressed with how it comes up with really creative solutions. I know this might sound crazy, but I want to apply this to SS to know if there can be some really unorthodox but efficient loadouts.
For those not familiar with machine learning or neural networks, it is basically like this:
- You start off with some random generated creatures (ships)
- In each following iteration, you determine a score for each creature
- Those that perform badly are each replaced by a mutation of a better performing creature
- After enough iterations, you will have a population of well performing creatures
Watch this series if you want to learn more
So in Starsector, you can start off with 100 random variants of a ship. In each iteration, you run a simulation of it fighting against some other ships. The score is determined by how much damage is caused to the enemy ship(s) and how much damage was caused to itself. 50 ships are eliminated, replaced by 50 mutations that have either a turret or hullmod swapped/removed/added. After some iterations, you should have 100 relatively well performing ships against that specific enemy type.
For now, this is just a thought. I'm not a mod developer, so I'm not sure if this is actually feasible. For example, I don't know if battles can be sped up much so that more iterations can be run, or whether the simulation module can be run independently and without graphical rendering.