Modeling Forms of Surprise in Artificial Agents: Empirical and Theoretical Study of Surprise Functions

Modeling Forms of Surprise in Artificial Agents: Empirical and Theoretical Study of Surprise Functions Luis Macedo (lmacedo@isec.pt) Department of Informatics and Systems Engineering, Engineering Institute, Polytechnic Institute of Coimbra, Quinta da Nora 3030-199 Coimbra, Portugal Centre for Informatics and Systems of the University of Coimbra, Department of Informatics, Polo II 3030 Coimbra, Portugal Rainer Reisenzein (rainer.reisenzein@uni-greifswald.de) Institute for Psychology, University of Greifswald, Department of General Psychology II, Franz-Mehringstr, 47 17487 Greifswald, Germany Amilcar Cardoso (amilcar@dei.uc.pt) Centre for Informatics and Systems of the University of Coimbra, Department of Informatics, Polo II 3030 Coimbra, Portugal surprise promotes both immediate adaptive actions to the unexpected event and the prediction, control and effective dealings with future occurrences of the event. Ortony and Partridge's (1987) model of surprise shares several aspects with the one proposed by Meyer, Reisenzein and Schutzwohl (1997), especially in that both models 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. Finally, models of surprise have also been proposed in the fields of knowledge discovery and data mining (e.g. Suzuki & Kodratoff, 1998). Macedo and Cardoso (e.g., Macedo & Cardoso, 2001)) developed a computational model of surprise that is an adaptation (although with several simplifications) of the models proposed by Meyer, Reisenzein and Schutzwohl (1997) and by Ortony and Partridge (1987). In the present article, we elaborate and evaluate this model further by discussing different possible functions for the computation of surprise and by evaluating these functions in an empirical study. The following section describes Macedo and Cardoso’s surprise model in more detail, including an overview of its theoretical background models. Subsequently, we discuss several possible functions for computing the intensity of surprise. Finally, we describe an experimental test that was carried out to evaluate the accuracy of these surprise functions. Abstract This paper addresses the issue of how to compute the intensity of surprise in an artificial agent. Resolution of this issue is important for the further specification of the computational model of surprise proposed by Macedo and Cardoso (2001) that was implemented in artificial agents “living” in a multi- agent environment. This model of surprise is mainly rooted in the cognitive-psychoevolutionary model of surprise proposed by the research group of the University of Bielefeld (Meyer, Reisenzein, & Schutzwohl, 1997) and in proposals by Ortony and Partridge. We propose several possible functions to compute the intensity of surprise. To assess their accuracy, they were evaluated in an experimental test that focused on the comparison of surprise intensity values generated by artificial agents with ratings by humans under similar circumstances. Introduction Considered by many authors a biologically fundamental emotion (e.g.: Ekman, 1992; Izard, 1991), surprise may play an important role in the cognitive activities of intelligent agents, especially in attention focusing (Izard, 1991; Meyer et al., 1997; Ortony & Partridge, 1987; Reisenzein, 2000b), learning (Schank, 1986) and creativity (Boden, 1995; Williams, 1996). Psychological experiments conducted by Meyer, Reisenzein and Schutzwohl provide evidence that surprising-eliciting events initiate a series of mental processes that (a) begin with the appraisal of a cognized event as exceeding some threshold value of unexpectedness or schema discrepancy, (b) continue with the interruption of ongoing information processing and the reallocation of processing resources to the surprise-eliciting event, and (c) culminate in the analysis and evaluation of that event plus immediate reactions to it and/or schema (belief) updating/revision. According to these authors, surprise has two main functions, the one informational and the other motivational: it informs the individual about the occurrence of a schema-discrepancy, and it provides an initial impetus for the exploration of the unexpected event. Thereby, Surprise Model As mentioned, the surprise model developed by Macedo and Cardoso (2001) is mainly based on Ortony and Partridge’s (1987) proposals and on those of Meyer, Reisenzein and Schutzwohl (1997). Therefore, we first give an overview of these background theories and then explain the computational model proposed by Macedo and Cardoso, by comparing it with these two models.