Changes in Trajectories of Foraging Agents Under Spatial Learning

Abstract

The goal of this thesis is to identify differences and consistencies in the trajectories taken by foraging agents before and after they have learned the location of a target. The challenge is that these agents do not go directly towards the target after learning and keep a certain amount of randomness in their paths. We use different versions of discrete curvature and head angle as tools in this analysis. We also build models of foraging agents using stochastic processes with data supported parameters.