Towards Artificial Forms of Surprise and Curiosity Luís Macedo Instituto Superior de Engenharia de Coimbra / Centro de Informática e Sistemas da Universidade de Coimbra Quinta da Nora 3030 Coimbra PORTUGAL +351 39 790321 lmacedo@isec.pt Amílcar Cardoso Departamento de Engenharia Informática da Universidade de Coimbra /Centro de Informática e Sistemas da Universidade de Coimbra Pinhal de Marrocos 3000 Coimbra PORTUGAL +351 39 790000 amilcar@eden.dei.uc.pt ABSTRACT 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. Keywords Evaluation of Creativity, Surprise, Curiosity. INTRODUCTION Artificial Intelligence, one of the disciplines of Cognitive Science, attempts to understand and build intelligent agents. Particularly, one of the approaches of Artificial Intelligence aims to understand and build artificial agents that act and think like humans (Russel & Norvig, 1995). In order to accomplish this task, in addition to other human features, such an agent should become surprised with some parts of its environment that it did not expect, and it should become curious with unknown (new) things of the world. These unknown things trigger actions in the agent in order to study and know more about them. The basic definition of surprise says: "to encounter suddenly or unexpectedly"; "to cause to feel wonder, astonishment, or amazement, as at something unanticipated". This means something unpredictable, unanticipated or unexpected causes to feel surprise. Moreover, surprise has been related to the phenomenon of creativity (e.g., Boden, 1992; Boden, 1995; Macedo et al., 1998; Macedo & Cardoso, 1998). It has been more often pointed as a consequence of perceiving a creative object. Considering that a creative product has been described as comprising originality (previously defined as the unexpected novelty) and appropriateness (defined as usefulness, aesthetic value, etc.), in this context surprise happens when an original and appropriate object is perceived (Macedo & Cardoso, 1998). Curiosity is defined in a dictionary as: "the desire to know or learn an object that arouses interest, as by being novel or extraordinary". This means novel and possibly unexpected objects stimulate actions to acquire knowledge of those objects. These actions usually comprise, firstly, the focus of the senses on the unknown object. For instance, there is no doubt that humans usually focus their eyes in the new objects of an environment. Actually, when faced with a set of objects they are more attracted by new objects, and even more if they are both new and appropriate. Objects that are familiar to the agent do not attract them as new ones do, at least for a few moments. There is no doubt that the world comes into the human mind trough the senses. The sensory world is somewhat transformed and represented in the mind, in what is called mental representation of the world (Eysenck & Keane, 1990). How human mind represents the world is one of the big questions that have been challenging psychologists, philosophers, linguistics, etc., for centuries. Nevertheless, several approaches to mental representation have been proposed. They play a central and essential role in knowledge-based intelligent agents, and consequently when modelling them, as for example in reasoning,