When dealing with the problem of making highly scalable systems, we often have to forgo a number of luxuries. In the Movable Feast Machine (MFM), a simulated architecture for an infinite computer, some of these sacrifices make traditional computation difficult; a lack of global scope, a highly limited local scope, and asynchronous execution all complicate a number of traditional computing problems.

We took on the problem of finding other elements in a sparsely populated system using an active sensing system, in which we have a “conflict” between various colonies - like ant hills - which have to find and destroy each other. The scouts from each colony leave behind breadcrumbs, like pheromone trails, which can then be “activated” to lead other ants to the colony in order to capture or destroy it.

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.