Ok, so since starting this discussion I have read many more journals, articles and discussions on NN's. My conclusion is that NN's are not an absolute solution, BUT they can help form a solution quite nicely.

Another thing I found with NN's is their problem specific, I think alot of people got narky because I mentioned using a NN for automating an agent. Ok, so its not the best way to solve that PARTICULAR example. Now consider you had a game (NERO is an example) where your given a team of soldiers and you have to train them by putting them through military training exercises of your choice. Once you have trained your soldiers to a level your happy with or set period of time, you are then placed in a battlefield with another human players set of soldiers and see who wins. Correct me if im wrong but the only way to create a game like this would be using neural networks?? Not only this, as Risto Miikulainen said in his paper "Creating Intelligent Agents in Games" you are now starting to create new genres of games (which in my opinion the industry needs)

Quote:Original post by sion5Now consider you had a game (NERO is an example) where your given a team of soldiers and you have to train them by putting them through military training exercises of your choice. Once you have trained your soldiers to a level your happy with or set period of time, you are then placed in a battlefield with another human players set of soldiers and see who wins. Correct me if im wrong but the only way to create a game like this would be using neural networks??

No, there are other ways of creating general, adaptive behaviour. Remember, neural networks can't do anything that can't be done using other methods. For training combat agents in an RTS-like game, I'd probably prefer Bayesian networks. Support vector machines using the kernel trick is another alternative that is often preferred over NN's. I'm sure there are several more alternatives available.

Quote:Original post by sion5
Truth is academia is there to encourage innovation. I'm sorry but anyone can work in a factory pushing out the same product one after the other, but it takes academics to say "Hey wait, surely this can be done better?".

This is such a load of arrogant crap it is bordering on invalidating the usefulness of this entire thread and disqualifying you from further consideration on any relevant subject matter. 99% of the innovation in the modern world has come from outside academia. In the games industry, much the same can be said.

Quote:Graphics and Audio has come on leaps and bounds in the past few years but where is A.I??

Because they are vastly different problems. The innovation and improvement curves are not going to be similar.

Quote:If everyone's attitude is that we have found the best solution then there will never be an advancement in this domain.

Who is this "everyone" of which you speak? Every single time I sit down to work on my stuff, I'm trying to do something better. Every time I crack open the new AI Wisdom book, I see something from a front line dude that makes me say "damn... someone found a better way" - and usually it was because they were trying to solve a problem in their own projects. (You have read all the AI Wisdom books, right?)

Quote:Back to subject. Are there any readers who are working on/ have worked on high profile games that have tried using NN technology?

There used to be material on that in the early AI Wisdom books... but not in the most recent one. It just hasn't caught on because people haven't found a use for it. Coincidentally, those articles often come from academic individuals and teams and NOT from the industry peeps.

Seriously... my suggestion to you is to do a little more reading. Do a little more browsing the web. The information is not going to come to you here. You are going to need to go to the information. If you want to give the forums at AIGameDev a try, have at it - there's a more active community there with a lot of pros... but I'm quite sure you will get a similar message.

An interesting exercise for you would be this... make a list of all the possible things you think an AI agent needs to do in a game. Pathfinding, steering, animation control, cover processing, decision making, planning, state management, cooperative interaction, etc... Then cull it down to the stuff that could be dealt with by a NN. Then list all the other ways that the same type of thing could be dealt with. List the pros and cons of each method. (if you don't know, you are already working from behind.) Then cull that down by what sorts of requirements the production game space needs. (e.g. computation speed, production speed, predictability, control, stability, etc.) Rank accordingly. This may be rather illuminating.

We've been trying to tell you... but after all, we aren't academics like you - therefore we don't know shit. It's up to you and your fellows to change the world. We just turn wrenches.

Quote:Original post by Hnefi

Quote:Original post by InnocuousFox
Regardless of the tool, any decision system is at the mercy of how many inputs are hooked up to it. If you fail to include an input as a possible critieria and yet that piece of information becomes the difference between two otherwise similar scenarios, your agent will not know what to do. Again, this is regardless of the tool used - NNs, BTs, HFSMs, whatever. It's a knowledge representation issue first.

I'm not sure I understand what you mean. Neural networks are strictly signal processors; their input domain is perfectly defined.
[snip]
I don't see how it can be a knowledge representation issue, because NN's do not model knowledge explicitly. NN's deal strictly with signals, not abstract representations.

That's the point. Make a list of all of the inputs that you would require to make a decision about a certain problem. Those are your inputs, correct? What if you were to take one away? The decision model wouldn't be able to take that into account - and the resultant decision could suffer accordingly.

So, back with your original list, what if you haven't thought of everything? There may be a contingency where that one extra input that you forgot to include is the tipping point between stupid decision A and good decision B. However, your agent is stuck with stupid decision A because it doesn't know any better. The only knowledge that your agent had of the world was what you chose to provide it. Even if it is processing that information perfectly, it could still look stupid because of something you forgot.

(Note: this is for any decision tool... not just NN's)

Quote:Original post by sion5
Ok, so since starting this discussion I have read many more journals, articles and discussions on NN's. My conclusion is that NN's are not an absolute solution, BUT they can help form a solution quite nicely.

YES! Holy crap... about freaking time. If you wanted to use an NN for a small, contained situation, sure. Of course, as mentioned previously, it's likely that similar sort of decisions could be made by RBS, Expert Systems, Bayesian Networks (especially dynamic ones), etc. But at least you are out of the "Holy Grail" mentality.

Quote:Original post by InnocuousFox

Quote:Original post by Hnefi

Quote:Original post by InnocuousFox
Regardless of the tool, any decision system is at the mercy of how many inputs are hooked up to it. If you fail to include an input as a possible critieria and yet that piece of information becomes the difference between two otherwise similar scenarios, your agent will not know what to do. Again, this is regardless of the tool used - NNs, BTs, HFSMs, whatever. It's a knowledge representation issue first.

I'm not sure I understand what you mean. Neural networks are strictly signal processors; their input domain is perfectly defined.
[snip]
I don't see how it can be a knowledge representation issue, because NN's do not model knowledge explicitly. NN's deal strictly with signals, not abstract representations.

That's the point. Make a list of all of the inputs that you would require to make a decision about a certain problem. Those are your inputs, correct? What if you were to take one away? The decision model wouldn't be able to take that into account - and the resultant decision could suffer accordingly.

So, back with your original list, what if you haven't thought of everything? There may be a contingency where that one extra input that you forgot to include is the tipping point between stupid decision A and good decision B. However, your agent is stuck with stupid decision A because it doesn't know any better. The only knowledge that your agent had of the world was what you chose to provide it. Even if it is processing that information perfectly, it could still look stupid because of something you forgot.

(Note: this is for any decision tool... not just NN's)

I think I understand what you're trying to say now. Basically, if the domain is not structured as well as it could be, the decision process will suffer. I agree, but beside being trivially obvious, it doesn't really have anything to do with dealing with unforeseen situations which is what I was responding to shurcool about.

But sure, as previously stated, preprocessing/input configuration has a great effect on the performance of neural nets.