Posted 23 July 2008 - 01:26 AM
Just set up a small framework and try to solve some problems. When I did this, I was amazed at how bad it actually works. It's by far not as easy as pluging some test data in, and after 4 hours of learning s.th. usefull comes out.
So as I said, do some testing and you will see, what they are good at and when they are simply a pain in the ass.
As of NNs beeing able to respond to untrained, new scenarios: I highly doubt this works, or at least works better than the "old-school" algorithms.
As was already stated, NNs are good at pattern recognition, where "patterns" would be specific situations in your case. Now the NN will be fine at recognizing the pattern, even if there are small changes (like an enemy more or less, a bit more or less health, ...), but as soon as something completely weird happens, eg. a pattern the developer didn't think of and there for didn't train, it won't know what to do.
Example: If anyone here played Crysis, go ahead, run into an enemy camp, climp on a tower and wait there, the AI won't know what to do, because the devs didn't do much scripting for that scenario. Now, if an NN was used, it would be pretty much the same thing, it wouldn't know what to do for this pattern, as the devs didn't train it. I can't imagine the NN would take it's chewing gum and a pair of boots, and build an rocket launcher from that to blast you down that tower.
Now if you still want to stick with NNs I would try a modular approach. Hnefi already said, that preprocessing the data helps a lot. I would try a network of NNs, with a NN for each specific task. For example a bot would have an NN for target prioritizing, an NN for abstract decision making, an NN for targeting, an NN for movement, ...
That way you can train each module individually and put all parts together in the end, wireing outputs of higher-level modules to inputs of lower-lever ones. Debugging also gets easier, cause if something goes wrong, you can check the outputs of each module to see who fucked up.
However what you get in the end (if it works) has the same functionality as decisiontrees so there is no real benefit.
The only thing that could help NNs is to put some work in learning. If you find a good and fast way of learning from past mistakes, you could try to implement an AI for an RTS game.
Maybe again with a modular approach (eg. one NN for enemy prediction, one NN for building, one NN for general strategy, one NN for mikro-movement, ...). Or some sort of hybrid approach with an old-school AI managing stuff, but an NN that can steer some weights inside the hardcoded AI and serve as "intuition".
However in both cases with the ability to learn from the match by "looking at the replay" or s.th. and figure out a way to perform better in the future.
It would be really cool, having an AI not falling for the same trick over and over again.
I'm pretty sure this can be done way easier without NNs but you never know...
I guess this wasn't much of a help, but you really picked a tough topic and you should really keep your expectations low.
On the other hand, I quite like these discussion-threads, as they are usually an interesting read...
I'm not that much into NNs but my advice would be to do some testing. There are libs like FANN that can be used to set up and train NNs without much knowledge and to play with them.