It's clear that the effectiveness of a neural network depends strongly on the format you give it to work with. You want to pre-process it into the most convenient form you can algorithmically get to, so that the neural network doesn't have to account for that itself.
I'm working on a little project that is going to be using neural networks. My future goal is to eventually use
NEAT, which I'm really excited about. Anyway, one of my ideas involves moving entities in continuous 2D space, from a top-down perspective (this would be a really cool game AI). Of course, unless these guys are blind, they're going to be able to see the world around them.
There's a lot of different ways this information could be fed into the network. One interesting but expensive way is to simply render a top-down "view" of things, with the entities as dots on the picture, and feed that in. I was hoping for something much simpler to use (at least at first), such as a list of the x (maybe 7 or so) nearest entities and their position in relative polar coordinates, orientation, health, etc., but I'm trying to think of the best way to do it.
My first instinct was to order them by distance, which would inherently also train the neural network to consider those more "important". However, I was thinking- what if there's two entities that are nearly the same distance away? They could easily alternate indexes in that list, confusing the network.
My question is, is there a better way of representing this? Essentially, the issue is the network needs a good way of keeping track of who's who, while knowing (by being input) relevant information about the list of entities it can see.