I’m experimenting with AI and generally want to explore different models for programming agent behavior and while neural networks and such are cool, their training nature makes it hard to set rules, so I assume they aren’t very good for creating actual behavior of an agent.
I’m not particularly experience with AI, but it seems to me that every established option for game AI is fairly limiting.
For example, for my test project, I wanted to make a functional village simulation in which agents keep the needs of it satisfied without player’s intervention. This seems like a perfect fit for a GOAP or HTN model, because clear goals can be given to each agent:
[Fish for food] = Move to nearest fishing spot → Fishing activity → Move to village storage → Unload inventory
[Repair building damage] = Move to village storage → Load inventory → Move to damaged building → Repair activity
And it works good on a macro scale, but I also want agents to respond to ongoing changes in the world. For example a response to the player approaching, whether they’re holding a weapon or not, etc.
Now, being “reactive” is a clear indicator that behavior trees might be the answer, but I’d like to retain the action planning as well.
I understand that “action planning” described above can also be implemented in a behavior tree, but keep in mind that I gave a very simplified example. In order to gather wood, for example, an agent might need to cross a river to reach the forest, and for that it will need to find a way to cross it, whether it be by boat, bridge, swim or anything else.
I’d appreciate any insights from devs familiar with AI design. Should I combine BTs with a form of action planner? Use something else entirely?