← Thread:Talk:Random Targeting/The advantage/reply (3)
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Assuming the random targeting is using Precise MEA, Nene might actually be hit less than SittingDuck. That's because the Precise MEA increases if you are moving at the time of fire, because it is faster to decelerate than accelerate.
But yes, essentially with a random gun the probability of being hit is botWidth/fireWidth (both in radians), which isn't affected by whether or not it always happens at the same place or not.
I'd actually considered sticking a Precise MEA Random gun in DrussGT just to future-proof it against any crazy new surfing tech which might spring into place ;-) Also, against a top surfer with a flattener, a Precise-MEA random gun works about as well as a state-of-the-art AntiSurfer gun...
And this conversation got me thinking about it again. =) But it would have to significantly outperform my AS gun vs DrussGT/a few other bots to make it worth my while. I doubt we'll see the day any surfing can really destroy a decent AS gun to the point RT would be viable - targeting just has way too large of an advantage in the amount of information it receives.
I think I might see your point.
While you are more likely to hit a robot at 0 if you only aim at 0. Also if you aim at -0.5 to 0.5 you are more likely to hit 0 then if you aimed at -1 to 1.
The reverse is not true. If you aim -1 to 1, you will be just as likely to hit that robot as a robot that moved -1 to 1. This is a bit counter-intuitive (to me). But I never actually studied probability. But it does make sense in a way.
But wouldn't your chance of hitting increase if you only targeted the gfs that an enemy robot actually went?
But that would be more of a fuzzy logic gun (it is usually somewhere in here) then a random gun. It enters into the realm of a real gf gun if you fire at gfs locations where the enemy is more often.
So a weighted-random gun Chase? Could be interesting, though against a sufficiently advanced flattener it could perform worse than a "plain" precise-MEA random targeter.
Maybe an interesting approach would be to give a targeting system a dynamically adjustable "randomness" factor, where 0.0 is a conventional guessfactor targeting, 1.0 is precise-MEA random, and somewhere inbetween would be weighted random. Perhaps it would be possible to use some metrics calculated from opponant behavior to determine the optimal "randomness" factor.
Like Voidious says, targeting does have a huge information advantage over surfing, but at the same time with a deterministic targeting algorithm, if the surfing knew the exact targeting algorithm it was facing it could dodge everything perfectly, which makes me wonder if there is perhaps room for a measured dose of randomness.
Any deterministic targeting strategy has a counter dodging strategy which can dodge it near-perfectly. It can´t be 100% perfect because radar takes a few ticks to lock.
In theory, you can put many specialist dodging strategies in your bot and choose one based on opponents name. And then, dodge all top bots near-perfectly.
But specialist algorithms are not as fun as generalist learning ones, so, almost no one does it. In nanorumble however, LittleBlackBook does.
Combat uses random targeting while the targeting real-wave history is empty. It is there to counter some starting strategies, like staying still until an incoming wave is detected. Yes, it misses shots against SittingDuck, but preserves above 0% hitrate against Toorkild in the beginning of the first round.