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I am running a physics simulation and applying a set of movement instructions to a simulated skeleton. I have multiple sets of instructions for the skeleton consisting of force application to legs, arms, torso, etc. and duration of force applied to their respective bone. Each set of instructions (behavior) is developed by testing its effectiveness performing the desired behavior, and then modifying the behavior with a genetic algorithm with other similar behaviors, and testing it again. The skeleton will have array behavior in its setlist.

I have fitness functions that test for stability, speed, minimization of entropy and force on joints. The problem is that any given behavior will work for a specific context. One behavior works on flat ground, another works if there is a bump in front of the right foot, another if it's in front of the left, and so on. So the fitness of each behavior varies based on the context. Picking a behavior simply on its previous fitness level won't work because that fitness score doesn't apply to this context.

My question is, how do I program to have the skeleton pick the best behavior for the context? Such as picking the best walking behavior for a randomized bumpy terrain.

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I believed that the "terrain" information you have for your model was very approximate and large-grained, e.g., "smooth and flat", "rough", "rocky", etc. and perhaps only at a grid level. But, if the world model is very specific like from a simulated version of a 3-D laser range scanner, then algorithmic and computational path/motion planning methods from robotics are likely to be further useful than a machine-learning classifier system.

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