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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.
I'd say "use distance from other opponents too" and "force coming from an Anti-Gravity calculation" are similar to what I used in Glacier's targeting actually. I used several dimensions which (more or less) measured the closest distance to another bot at different angle ranges.
With anti-grav systems, different ones will weight things differently, but in all cases the most prominant influences are the closest ones. As such, my goal was to create a measure with a small finite number of dimensions, that characterized the most prominant influences meaningfully.
In any case though, all those methods don't really consider the field-wide interactions that can occur, where movements can cause a chain reaction. For that, I doubt there is much of a reasonable option besides some sort of iterative process (maybe not per-tick, could be larger steps or iterations that are not time-based, but yeah).
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.
