[programd]

The whitepaper on the Grail framework is interesting [1]. It's an AI C++/C# framework you can use in game development.

I would call it a very good implementation of "old school" AI, where the behavior of your actors is all about utility curves, Monte Carlo search, and genetic algorithms. Basically all math/algorithm based stuff, kind of like old expert system AI implementations.

Of course "new school" AI is all about neural networks that can automatically learn those complex actor behaviors without the developer explicitly specifying all those mathematical algorithms.

Like many people here I'm very interested in working on hooking up modern "new school" AIs to virtual worlds, so it's very interesting to see Grail as a good concrete example of the algorithmic approach to game AI. I suspect some hybrid fusion of both approaches may give us some interesting and fun AI behavior.

[1] https://grail.com.pl/media/Grail_Whitepaper_June_2021.pdf

[uoaei]

The dichotomy of interest isn't really NNs vs GAs but about stochasticity vs determinism. The user/player doesn't care which it is until they learn to see the patterns and to reliably exploit the rules. Once you notice these patterns, the spell is partially broken and immersion is compromised. That's part of the reason stochastic methods have been such a hit throughout the history of game development for NPC behavior.

[bob1029]

I think things get really interesting when you combine small, efficient ANNs with evolutionary/genetic algorithms.

The biggest challenge with these is coming up with a function that clearly represents the fitness of a candidate. EAs are worthless if you can't provide good selection pressure over time. There are always multiple conflicting objectives in the most practical cases. I've recently discovered that you can skip all the nasty objective weighting business if you just select the Pareto front every generation. You never know when a trip down a less important path might result in the most optimal global solution.