So, the idea is that what if we have a hierarchy where some of the agents in the hierarchy have multiple supperiors. Then the underlying gets tasks from both its superiors.  How does it learn how much weight to put on each of the tasks.  How does it know which to do first?  I think that maybe if the hierarchy backpropagated the error (sort of like in a neural network) that the agents could learn to weight the various (inputs) commands.

Another idea is to use HyperNEAT to evolve the hierarchy.  The substrate is defined as the fully connected hierarchy, y-axis is rank and the x-axis is the id of the agent.  These coordinates get plugged into the CPPN which will then output the weight for that connection?  This would then allow a malleable hierarchy to be constructed.  Allowing agents to move in the substrate persists the weights!

It would be awesome if HyperNEAT wasn’t a centralized approach to the process.  That should be the next step.  How to do a similar procedure where the agents have more autonomy…?