Adaptive Emotion Based DecisionModel for a Social Robot

This thesis introduces a computational model of emotions and decisions for a robot, which interacts meaningfully in a social context. The decision making framework is based on multi-attribute utility theory, but it contains a dynamic and adaptive emotional model which basically acts as a preference and perception manipulator. The emotional model is based on event appraisal with discrete emotion categories. Events are assessed using dimensions of utility and probability as well as expectations. The model uses the concepts of core affect and attributed affect to create a multilevel emotion consisting of moods and emotional events. Personality traits are used to create different emotional dynamics by modifying relevant parameters. Attitudes and relationships, understood through attributed affect and classical conditioning, make the robot emotions more believable. The robot learns from user actions and makes predictions about them and environment changes according to prob-abilistic models. Subjective well being and human need hierarchy are used as the basis for the preferences which the visceral state affects. The model is inspired by the computational models Cathexis, FLAME, EMA, TAME and Robocep-tionist, and is an expanded version of the model used in AISoy1 robot. The framework combines extensive psychological research and requires validation .