Towards Artificial Forms of Surprise and Curiosity

This paper addresses the issue of modelling human forms of surprise and curiosity in an artificial perceptual agent. The following statements support our approach: humans are surprised when they perceive something that they did not expect; humans feel desire to learn more (i.e., feel curiosity) about novel (possibly unexpected) objects, usually manifested by focusing the senses on those objects in order to study and analyse them. We describe how external world is internally represented through graph-based representations in our model. In order to accomplish the task of modelling human surprise and curiosity, we describe two main measures: the measure of the difference (or novelty) of an object and the measure of the degree of not expecting an object. Based on these measures we propose approximate mathematical functions for surprise and curiosity. This approach is illustrated with an example of a perceptual robot that autonomously explores and studies its environment.