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Paul wrote: > I've been working on my RLG, Fugue, for the past few years, and while > it hasn't reached the megalomaniacal ambitions of Armok, it's pretty > hefty for a first try. Ok, now that I've covered that, here's the > problem. I'm looking at doing this with some neural nets in > strategically placed locations in the AI so that it won't be brain > dead. Unfortunately, I've been trying to deal with the problem of > evaluating the current situation. Wow, neural networks are cool but I think they need some pre-work before you can use them. Step 1 : choose your inputs : typically that would be - the grid that can be seen/is known by the 'monster' - percent health monster - perceived health hero - health difference monster since previous turn ( either healing by monster or getting damage by hero ) - perceived health difference since previous turn ( either healing by hero or getting damage by monsters ) - monster can do distance attacks even when stuff is in the way( bool ) - monster can do distance attacks if stuff is not in the way( bool ) - hero can do distance attacks even when stuff is in the way( bool ) - hero can do distance attacks if stuff is not in the way( bool ) Step 2: Then you have to make a borg, make it as powerfull as it will generally be when they meet the monster. Step 3: Define fitness condition : usually go for kill hero without getting killed, or just not getting killed. Step 4: The Darwin Machine : Run and monitor your neural nets, do random changes to the weights and inhibitors and raise your neural networks to clever little beings which will annoy the players. Step 0 : Buy the book Blondie24, it gives insight to neural networks and deals with neural networks analying a grid of checkers, which ( with some fantasy ) can be used for analyzing a dungeon floor. > Thanks in advance. > > -- Paul T.
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