Question

I’m considering using a neural network to power my enemies in a space shooter game i’m building and i’m wondering; how do you train neural networks when there is no one definitive good set of outputs for the network?

Answer 1

I’m studying neural networks at the moment, and they seem quite useless without well defined input and output encodings, and they don’t scale at all to complexity (see http://en.wikipedia.org/wiki/VC_dimension). that’s why neural network research has had so little application since the initial hype more than 20-30 years ago while semantic/state based AI took over everyone’s interests because of it’s success in real world applications.

• A so a good place to start might be to figure out how to numerically represent the state of the game as inputs for the neural net.
• The next thing would be to figure out what kind of output would correspond to actions in the game.
• think about the structure of neural network to use. To get interesting complex behavior from neural networks, the network almost has to be recurrent. You’ll need a recurrent network because they have ‘memory’, but beyond that you don’t have much else to go on. However, recurrent networks with any complex structure is really hard to train to behave.
• The areas where neural networks have been successful tend to be classification (image, audio, grammar, etc) and limited success in statistical prediction (what word would we expect to come after this word, what will the stock price be tomorrow?)

In short, it’s probably better for you to use Neural nets for a small portion of the game rather as the core enemy AI.