Computational Affective Science

In the target article, Hudlicka points out very clearly that there are several key challenges in affective computing in general and computational modeling of emotion in particular. The lack of guidelines and the sharing of best-practices as well as failures are some of those challenges. These challenges are very relevant when one is interested in the development of computational models of emotion that are focused on being applied in particular domain, for example games, virtual avatars or social robots [1]. As Hudlicka shows, choices and assumptions have to be made when developing a computational model based on, e.g., an appraisal theory. More often than not, these choices and assumptions are left unaddressed in the final publications, and rarely are the object of experimental investigation, while it is precisely these choices that make the computational model run in the first place (e.g., some representation of a goal is needed, but how is a goal represented in the agent?). Hudlicka provides an impressive overview of the different choices one has to make and the different options one has when computationally modeling emotion. It is therefore interesting to note that for many of these options it is unclear, not due to Hudlicka’s overview but due to the lack of experimental data, why one should pick one or the other if one is interested in grounding the option in emotion psychology. For example, how and when to integrate (mix) emotions is a thorny issue that is far from solved. If one implements the OCC model [2] it is not clear how to mix emotions into the emotional state [3], and it is also not clear how the “appraisal tree” should be traversed computationally. A very simply choice between breadth and depth first already changes the order of affective attributions that arise given the appraisal of a specific situation. Also, the way the agent deals with how a belief is represented as well as how events are processed (in parallel, serial, prioritized, etc.) will influence the order of appraisal. So, it is without doubt that Hudlicka touches upon a major challenge in the computational modeling of emotion when she states that guidelines regarding how to model a particular affective phenomenon of interest are needed.