A controlled experiment: Evolution for learning difficult image classification

Abstract

The signal-to-symbol problem is the task of converting raw sensor data into a set of symbols that Artificial Intelligence systems can reason about. We have developed a method for directly learning and combining algorithms that map signals into symbols. This new method is based on evolutionary computation and imposes little burden on or bias from the humans involved. Previous papers of ours have focused on PADO, our learning architecture. We showed how it applies to the general signal-to-symbol task and in particular the impressive results it brings to natural image object recognition. The most exciting challenge this work has received is the idea that PADO's success in natural image object recognition may be due to the underlying simplicity of the problems we posed it. This challenge implicitly assumes that our approach suffers from many of the same afflictions that traditional computer vision approaches suffer in natural image object recognition. This paper responds to this challenge by designing and executing a controlled experiment specifically designed to solidify PADO's claim to success.