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[XJIE6yIIIEK]

Hi, I've been trying to find some good detailed books on NavMesh for a few days but so far was able to find only small articles without detailed explanation on how it actually works underneath and what are limitations of, for example, Unity realization. Can you suggest good books or lectures or anything that's really explaining it on algorithmic/math level. Thank you in advance.

[fluffrabbit]

Dunno about books. NavMesh isn't some revolutionary experimental process like deep learning, it's already very optimized and approached more from the perspective of engineering than science. You should look into Recast if you haven't already.

[qMopey]

NavMesh isn't some revolutionary experimental process like deep learning

I know wheat you mean here - ML is quite useful now and the usefulness is quite cool.

But I just couldn't resists posting... The ML algorithms are not really advanced much at all from the training algorithms that popped up in the 70's-80's. Really the only difference between old "neural nets" and "deep neural nets" is adding more hidden layers, which became practical with advancements in hardware, not in the software algorithms themselves.

As for nav mesh, I've actually never really used one myself. I'm assuming the general idea is to triangulate and then simplify your mesh, and once done, run some other path-finding algorithm? Or in other words, use mesh topology and geometry algorithms to generate "nice" data for path-finding to consume?

[fluffrabbit]

The ML algorithms are not really advanced much at all from the training algorithms that popped up in the 70's-80's. Really the only difference between old "neural nets" and "deep neural nets" is adding more hidden layers, which became practical with advancements in hardware, not in the software algorithms themselves.

I know what you mean. I'm very inspired by Wobbledogs and I'm working my way through the genetic algorithm tutorial linked from a post on how Wobbledogs does it. Unforunately, classic GA/EA has a piece missing - the brain. I'm afraid that once I get GA into Cave Confectioner, nobody will care because all it can do is optimize where the cavemen go to get food and whatnot, rather than evolving behaviors. If I put a neural network in the chromosome struct, it would be limited to a fixed size and probably require gated units to keep itself sane (LSTM, GRU, etc.) It would require more code and I don't know how to use neural networks yet.

Ideally I would use NEAT, which is like a neural network in a genetic algorithm except the neural network grows to the needed size. That's the state of the art right now.

As for nav mesh, I've actually never really used one myself. I'm assuming the general idea is to triangulate and then simplify your mesh, and once done, run some other path-finding algorithm? Or in other words, use mesh topology and geometry algorithms to generate "nice" data for path-finding to consume?

I haven't used it either, but I do know that it's inspired by classic A* pathfinding, and in the case of Recast, it does pretty much what you just described except that a support library simplifies all the geometry automatically so the human doesn't have to fiddle with it. It's not classic orthodox NavMesh, it's NavMesh++.