gun tuning tangent

And a little offtop: also, imho for last few weeks, that statistical targeting is impasse (deadlock?) and next breakthrough may be in single tick playing forward. Especially in the light of the fact that totally annihilate of weak bots is more important, that destroy strong bots.

I disagree for a different reason... I think that's a bit of a false dichotomy, because I'd still classify the "single tick playing forward" methods as statistical targeting so long as the mechanism used each tick is still statistical. It adds another assumption to make each data point used more generally, but so do GuessFactors.

Really, what the technique provides, is denser data by making the assumption that on a given tick the opponant behaves in mostly-deterministic manner according to the attributes you're targeting based on. If your attributes are sufficiently complete, it should have a quicker effective learning rate.

I do think there is value in the "single tick playing forward" idea, but as-is it uses too much CPU, espescially if your targetting attributes are complex. I think one has to consider what it brings to the table and take advantage of it without making things so slow. My current view on the best approach, is that it would be doing larger number of ticks than one at a time (i.e. 10-tick-at-a-time iterative prediction).

I did not say, that behind ST-PIF must be kNN etc.:) Neural Networks may be used, for example. But actually yes - when i implement this, it was knn based. And you completly right: although this gun gets hit rate >95% against walls and >60% against crazy, it was unacceptable slow.

And I never said ST-PIF was always statistical, just that it doesn't have anything more to do with it being statistical or not than GuessFactors do (aka, nothing) :)

<random> Come to think of it, "Single-Tick" techniques and "GuessFactor" techniques have a lot in common... both "fold" data across lines of assumed symmetry. GuessFactors "fold" across the "front-versus-back" symmetry, whereas Single-Tick folds across a temporal symmetry of sorts.

GuessFactors have proven themselves highly beneficial, and Single-Tick techniques may also in the future, howver both techniques would perform sub-optimally when encountering something which violates the symmetry they assume. Unless the targeting attributes include something that differentiates front/back, GuessFactors will perform sub-optimally when faced with an opponent which treats them differently. Of course, it's difficult to take advantage of this in a major way I think.

Similarly the weakness of Single-Tick techniques is when an opponent treats different ticks differently due to something that cannot be detected in the targeting attributes. For most robots, even surfers, the assumption is probably good enough... but... in contrast to guessfactors... <evil>A cleverly designed semi-random multi-mode movement could be designed so that the movement path generated by a "single-tick" technique is never where it actually ends up ;) </evil></random>

Anti-Pattern matching comes to mind.

Have you tried using k=1? How does it compare then with something like regular kNN-PIF in terms of speed and hitrate?

Sorry, but i forgot details, everything that i remember i already wrote. Tomorrow i can publish that code, but i have no time in nearest future to liven up it

I guess K=1 would make ST-PIF have the same weaknesses as neural network based Pattern matching (non-statistical).

If a bot dodges 30% of the time going straight then turning to the right and 70% of the time going straight all the way, Neural Targeting averages both patterns and shoots slightly to the right, missing both patterns. In other words, it is awful against Walls.

Increasing K is what makes the gun choose the "straight all the way" pattern alone and achieve 70% hit rate.

Having a classifier which differentiates both patterns solves the problem (distance to corners against Walls).

If not, making many k-NN searches with k=1 gives you 30% of the ticks from one pattern and 70% of the ticks from the other pattern, and the resulting pattern is a 30%/70% mix of the two. It assumes a 3rd unseen pattern can be predicted from 2 previous patterns.

Should perform well against orbital movement, where turn rate changes gradually with distance and a 3rd unseen turn rate (or a zig-zag) can be predicted from 2 others. But perform bad against pattern movement, where patterns don't change gradually with classifiers.

I also thought on this problem and find out a possible solution: keep similar amount of data with different classes.