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I realise this isn't a programming Q&A site, so bear with me. I'm interested in finding out the kind of information someone who is good at a game naturally filters out of the vast information from the screen. A very basic example would be ignoring the background textures and not ignoring the textures of other payers characters.

It seems like it would be interesting to attempt to write a program that might highlight what is/isn't important and see how that compares to what players perceive as important. Does anyone know of any reference that might get me started on this task (don't worry about the code side of things, I'm talking about machine learning techniques etc.). I've completed the Stanford online machine learning course so I have a reasonable understanding of the principals, but I wonder if there are other, better suited techniques out there.

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You might want to try and narrow your question focus a bit. There are many aspects of AI. If you are specifically interested in the probelm of highlighting 'what is/isn't important and see how that compares to what players perceive as important' you could rephrase the question. –  Josh Jun 29 at 23:49
    
@Josh thanks! I'll do that when I'm on my laptop later today! –  FraserOfSmeg Jun 30 at 8:04
    
@FraserOfSmeg, before concerning yourself with the cognitive science end of things, you should gain some familiarity with historic and contemporary approaches to AI. I'd suggest starting with finite state machines, before moving onto more complicated things like decision trees and planning AI. This should be moved to stackoverflow. =) –  blz Jun 30 at 17:24
    
I think if the question is re-written to focus on 'what players perceive as important' and how to implement that in AI it is a very appropriate question for Cognitive Science. –  Josh Jul 1 at 3:33

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