Applications of flocking algorithms to input modeling for agent movement

Abstract

Simulation flocking has been introduced as a method for generating simulation input from multivariate dependent time series for sensitivity and risk analysis. It can be applied to data for which a parametric model is not readily available or imposes too many restrictions on the possible inputs. This method uses techniques from agent-based modeling to generate a flock of boids that follow the data. In this paper, we apply simulation flocking to a border crossing scenario to determine if waypoints simulated from flocking can be used to provide improved information on the number of hostiles successfully crossing the border. Analysis of the output reveals scenario limitations and potential areas of improvement in the patrol strategy.