Exploration of Simulated Creatures Learning to Cross a Highway Using Frequency Histograms

Abstract

We study via frequency histograms, the behaviour of a model of simulated cognitive agents (creatures) learning to safely cross a cellular automaton based highway. The creatures have the ability to learn from each other by evaluating how successful other creatures in the past were for their current situation. We examine the effects of the model parameters on the learning outcomes measured through metrics such as the number of creatures that have successfully crossed. In particular, we focus on the effects of the knowledge base transfer on the creatures’ success of learning. The presented model is general enough so that the considered cognitive agent, called creature, maybe even interpreted as an abstraction of an autonomous vehicle (AV), encountering suddenly another moving vehicle on its trajectory. The AV has to decide whether to continue or to break/stop in order to avoid being destroyed.