Modeling Forms of Surprise in an Artificial Agent

Modeling Forms of Surprise in an Artificial Agent Luis Macedo (lmacedo@isec.pt) Instituto Superior de Engenharia de Coimbra / CISUC – Centro de Informatica e Sistemas da Universidade de Coimbra, Quinta da Nora 3031-601 Coimbra, Portugal Amilcar Cardoso (amilcar@dei.uc.pt) Departamento de Engenharia Informatica da Universidade de Coimbra / CISUC – Centro de Informatica e Sistemas da Universidade de Coimbra, Pinhal de Marrocos 3030 Coimbra, Portugal Abstract Mainly rooted in the cognitive-psychoevolutionary model of surprise proposed by the research group of the University of Bielefeld (Meyer, Reisenzein, Schutzwohl, etc.), the computational model of surprise described in this paper relies on the assumption that surprise-eliciting events initiate a series of mental processes that begin with the appraisal of unexpectedness, continue with the interruption of ongoing activity and the focusing of attention on the unexpected event, and end with the analysis and evaluation of that event plus revision of beliefs. With respect to the computation of unexpectedness, the model also incorporates suggestions by Ortony and Partridge. This model of surprise is implemented in an artificial agent called S-EUNE, whose task is to explore uncertain and unknown environments. The accuracy of our surprise model was evaluated in a series of experimental tests that focused on the comparison of surprise intensity values generated by the artificial agent with ratings by humans under similar circumstances. Introduction Roughly speaking, artificial and biological agents accept percepts from the environment and generate actions. Since different actions may lead to different states of the world, in order to perform well (to execute the “right” action), some kinds of artificial agents make use of a mathematical function that maps a state of the world onto a real number - the utility value. Thus, in those agents, decision-making is performed by selecting the action that leads to the state of the world with the highest utility (Russell & Norvig, 1995; Shafer & Pearl, Although research in Artificial Intelligence has all but ignored the significant role of emotions in reasoning/decision-making (e.g., Damasio, 1994), several computational models for emotions have been proposed in the past years, based in part on research in psychology and neuroscience (for a detailed review of those models see e.g., Pfeifer, 1988; Picard, 1997). Considered by many authors as a biologically fundamental emotion (e.g., Ekman, 1992; Izard, 1977), surprise may play an important role in cognitive activities, especially in attention focusing and learning (e.g., Izard, 1977; Meyer, Reisenzein, & Schutzwohl, 1997; Ortony & Partridge, 1987; Reisenzein, 2000b) (note however, that some authors, like Ortony, Clore, and Collins, 1988, do not consider surprise an emotion). According to the research group of the University of Bielefeld, Germany (e.g., Meyer et al., 1997), surprise has two main functions, informational and motivational, that together promote both immediate adaptive actions to the surprising event and the prediction, control and effective dealings with future occurrences of the event. Ortony and Partridge’s view of surprise shares aspects with this model, especially in that both assume that surprise is elicited by unexpected events. The same is also true for Peters’ (1998) computational model of surprise, implemented in a computer vision system, that focuses on the detection of unexpected movements. In this paper, we propose a computational model for surprise that is an adaptation (although with several simplifications) of the models proposed by the German research group of the University of Bielefeld and by Ortony and Partridge. The following section presents an overview of the overall agent's architecture into which the surprise model is integrated. Subsequently, we explain this model in detail. Finally, we describe experimental tests carried out to evaluate the accuracy of the surprise model. Overview of the Agent’s Architecture EUNE (Emotional-based Exploration of UNcertain and UNknown Environments) is an artificial agent whose goal is the exploration of uncertain and unknown environments comprising a variety of objects, and whose behavior is controlled by emotions, drives and other motivations. Besides desiring to know or be aware of the objects belonging to the environment, EUNE is also able to “feel” the emotions (including surprise)