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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.

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.