Its StatisticalTargeting properties should make sure that
Its PatternMatching properties ensure that
The new thing is that this targeting method basically zooms in (more PatternMatching) or out (more StatisticalTargeting) depending on the amount of data it has and the zoom level's segment size. Currently I use 8 zoom levels with increasing segment sizes. PatternMatching takes place in what I call the MicroSegments. In these tiny segments even only 1 previously reported angle is enough to deliver a firing angle with a high probability of hitting the enemy. StatisticalTargeting takes place when you zoom out, in the MacroSegments?. The segments here are much larger and contain more data to do statistical calculations with. Learning speed increases when the zoom level increases.
The PatternMatching algorithm that I use differs greatly from traditional PatternMatchers?. You might even argue if it is PatternMatching at all. I call it PatternMatching because it matches to a very specific situation. As a result it performs very well against bots that other PM guns perform well against :-). A situation is basically a very small segment with a lot of dimensions. Currently I use 6 dimensions, but if I can solve a memory issue I will use even more. I might even try a dimension that uses the movement history to make it a more official pattern matcher :-). In practise, this algorithm is a bit less accurate against truly predictable movers like SpinBot or PatternBot, but more effective against random movers. And since none of the top bots are truly predictable.........
There is a nice side-effect to using MicroSegments: I keep track of the number of Segments ('situations') that are used. This number increases more rapidly as the enemy has better movement. I am guessing this Segment count (say after 1000 rounds) could be a nice alternative Movement Index.
-- Vic Stewart
How far are you in developing this awesome gun?
Yeah, yeah, yeah..... You talk the talk, but does it walk the walk?
Sounds great, can I help in anyway?