The answer to your question will depend on what sort of neural network involved, and how it is being used. Neural architectures vary widely in their behavior and their selection involves trade-offs. What is probably more important, though, is what you want the network to learn, and how often you mean to update it.

Can you explain how you expect to use it?

-Predictor
http://will.dwinnell.com

Well, still with the chess game example, mmmm...
Let''s say the NN learns about moves that do more damage to the other side and some defensive positions (is that enough explanation?); it also learns about the importance of its pieces (and their effect on winning using some tactics).

About the updates, it could be about each game (or if its better, each turn).

Also let''s take a 2 layer (is that correct notation, it is in spanish) NN.

hes definitely right...it would take an incredibly long time to teach a NN to play chess (btw, its ok to call it a NN around here, we know you arent trying to teach some neurons in a petri dish to play chess...thats over on the bioengineering board) without preprogramming it. you certainly couldnt train it manually, youd have to play it against recorded games...and even then youd have to decide how "bad" a move the network just made...it wouldnt be fun and it wouldnt be fast.

besides, chess is a pretty easy problem to figure out...its completely deterministic and exact. its much much easier to do it this way (ok, relatively easier...you still need a supercomputer to sometimes beat kasparov).

anyway, there are many other avenues to pursue here, i just think with the game youre describing NN would not be a good solution. much like chess, i assume, you will have lots of pieces on the map...there are much easier ways.


and to nice coder: sometimes a greedy and back stabbing ai is the best solution to a problem...thats why humans have done so well on eath.

i think that would be really really interesting, purely from an ai investigation/frakenstein standpoint...your little creations going off in the world and doing ok for themselves.

unfortunately once again (and im totally not trashing your idea, just being practical), its a matter of time...even if you dont evaluate whole games, i guess you could stick them in at certain random points of saved games to practice the beginning, middle and end game, even then it would take an incredibly long time. imagine a population of even 100, creating the net, playing several moves (even more if you want to evolve *strategies*), 100 times, then making a new population.

furthermore, to get a full spectrum of strategies several seperate populations would have to evolve, then tested against one another at a much later point...and not to mention the huge network youd need to evolve any match even interesting to watch (just imagine the huge number of inputs and outputs[outputs for pieces and the moves they should make] then imagine *you* having to write the function relating the two...the problem space is mind boggling).

competitive populations are cool though...they get much more vicious...over fitting can be a problem though, unless, like i said, you have many seperate populations (or play against both other populations and pre-recorded games).

something like this might be interesting in an art installation, a year later you can come back and see how good they are after 12 months of competing, but it would probably not be practical in this sense.

quote:Original post by snyp
It still wouldn''t be good to include NNs in games because the technology maybe we have but still it wouldn''t be really fun, I did this as one of my projects and believe me, it first takes a whole lot of your time... it is not fun playing with the computer playing 1000 games of TTT and still it made mistakes... it did work though.

Why couldn''t one ship the game with the neural network already trained?

-Predictor
http://will.dwinnell.com