A model for learning global properties

Abstract

This paper reports developments in a mathematical model of cognitive learning. The model describes the processes of formation, testing, and revision of hypotheses held by a learner attempting to understand an environment. The fundamental assumption is that learning proceeds by a feedback cycle, where hypotheses are tested for validity against external reality and reweighted according to the outcome. A particular application of this model to the case of a mechanical "beetle", mapping a geometrical environment, yields results of interest in artificial intelligence and robot design, including results on the computability of several geometrical predicates.