Working in a spatially oriented system, creating a large network for communication or resource sharing is complicated by the difficulty of implementing useful data structures. We propose a node-based pathfinding system for use in a simulation where a number of central nodes attempt to engage in need-based resource allocation. We found that using spatially linked lists and a brownian search method made for a highly effective means of discovering routes and building a network organically in a chaotic environment.

REDO This method for pathfinding works well for sparse structures, demonstrating both an ability for the concept to work with a clear goal in the context of a more complex system (the conflict) and an ideal density level for the system to allow pathfinding to work well. If the machine is too sparsely populated, it can take scouts far too long to find their adversaries, while in a densely populated system the optimization (and eventually, the movement of the scouts) is severely limited. Additionally, when breadcrumbs persist too long, they can contribute to system clutter; I found that giving them a built-in maximum age of [X] helped mitigate this, as well as causing them to die after they are finished functioning for a single alert cycle.