Affective Goal and Task Selection for Social Robots

Effective decision-making under real-world conditions can be very difficult. From a purely decision-theoretic standpoint, the optimal way of making decisions – rational choice – requires an agent to know the utilities of all choice options as well as their associated likelihoods of succeeding for the agent to be able to calculate the expected utility of each alternative and being able to select the one with the maximum utility. Unfortunately, such rational methods are in practice often not applicable (e.g., because the agent does not have reliable or sufficient knowledge) or feasible (e.g., because it is too time-consuming to perform all necessary calculations). Psychologists have long hypothesized that humans are able to cope with time, knowledge and other resource limitations by employing affective evaluations (Clore, Gasper, & Conway, 2001) rather than rational ones. For affect provides fast, low-cost (although often less accurate) mecha-nisms for estimating the value of an object, event, or situation for an agent, as opposed to longer, more complex and more computationally intensive cognitive evaluations (e.g., to compute the ex-pected utilities) (Kahneman, Wakker, & Sarin, 1997). Humans also rely on affective memory, which seems to encode implicit knowledge about the likelihood of occurrence of a positive or negative future event (Blaney, 1986). Finally, affect also influences human problem-solving an