Swarming methods for geospatial reasoning

Geospatial data is often used to predict or recommend movements of robots, people , or animals (“walkers”). Analysis of such systems can be combinatorially explosive. Each decision that a walker makes generates a new set of possible future decisions, and the tree of possible futures grows exponentially. Complete enumeration of alternatives is out of the question. One approach that we have found promising is to instantiate a large population of simple computer agents that explore possible paths through the landscape. The aggregate behavior of this swarm of agents is a useful estimator for the likely behavior of the real-world system. This paper will discuss techniques that we have found useful in swarming geospatial reasoning, illustrate their behavior in specific cases, and discuss the convergence and application of such systems.