Effects of model parameter interactions on Naïve creatures' success of learning to cross a highway

Abstract

We investigate a learning strategy for a swarm of autonomous robots. We identify the robots with cognitive agents and describe a model of naive creatures learning to cross a highway. The creatures use a type of “observational social learning”, in which each creature learns from observing the outcomes of the other creatures that have crossed the highway. The learning outcomes are influenced by many of the simulation model's independent variables/parameters, which are the creatures' emotional states of fear and/or desire, their crossing point locations, the creatures ability or not to change a crossing point, the highway traffic conditions characterized by cars density, the duration of the creatures' learning process within the same environment (i.e., under the same highway traffic conditions characterized by cars density) and the transfer of the knowledge base acquired in one learning environment to another one (i.e., from an environment with one car traffic density to another one). We study how these factors, in particular their interactions, affect the model performance measured by the number of successful, killed and queued creatures and the creatures' ability to learn.