BerryBots updates

Instead of classifying data using a single opponent at a time, classify by all opponents at the same time.

When predicting opponent´s A behaviour, instead of using only opponent´s A distance and velocity as input, use distance and velocity from other opponents too. The same principle applying to any kind of classification.

I thought about this before, but didn´t know how to deal with eliminated opponents. Thinking again, now I have some ideas.

One thing I've tried is attributes based on the force coming from an Anti-Gravity calculation. I thought it was going to be a killer feature, but despite being fairly rigorous to make sure it was doing what I wanted, I never got a performance gain out of it.

It seems like there must be a way to leverage that data, though.

If everyone used anti-gravity movement, assigned and weighted points the same way, it would work wonders.

Combat uses anti-gravity movement, but weights points differently, and also assign anti-gravity points on enemy virtual bullets (shrapnel dodging), which are invisible to opponents. Good luck to anyone trying to guess the resulting anti-gravity force.

Many intermediate anti-gravity bots also assign random anti-gravity points accross the battlefield in order to confuse opponents.

Interesting. In Neuromancer I make some KNN attributes relative to the closest enemy, so distance, latVel, advVel. Then I use PIF so it doesn't matter that the attributes were from somebody else's perspective.

PIF as well here. Sounds to me like your attributes are more done with a low density of melee bots in mind (i.e. closest enemy having much more influence than the second and third closest), whereas my attributes are mode done with a high density of melee bots in mind. Interesting.